Multi-objective combinatorial optimization problems (MOCOP) frequently arise in practical applications that require the simultaneous optimization of conflicting objectives. Although traditional evolutionary algorithms can be effective, they typically depend on domain knowledge and repeated parameter tuning, limiting flexibility when applied to unseen MOCOP instances. Recently, integration of Large Language Models (LLMs) into evolutionary computation has opened new avenues for automatic heuristic generation, using their advanced language understanding and code synthesis capabilities. Nevertheless, most existing approaches predominantly focus on single-objective tasks, often neglecting key considerations such as runtime efficiency and heuristic diversity in multi-objective settings. To bridge this gap, we introduce Multi-heuristics for MOCOP via Pareto-Grid-guided Evolution of LLMs (MPaGE), a novel enhancement of the Simple Evolutionary Multiobjective Optimization (SEMO) framework that leverages LLMs and Pareto Front Grid (PFG) technique. By partitioning the objective space into grids and retaining top-performing candidates to guide heuristic generation, MPaGE utilizes LLMs to prioritize heuristics with semantically distinct logical structures during variation, thus promoting diversity and mitigating redundancy within the population. Through extensive evaluations, MPaGE demonstrates superior performance over existing LLM-based frameworks, and achieves competitive results to traditional Multi-objective evolutionary algorithms (MOEAs), with significantly faster runtime.

Designing effective algorithmic components remains a fundamental obstacle in tackling NP-hard combinatorial optimization problems (COPs), where solvers often rely on carefully hand-crafted strategies. Despite recent advances in using large language models (LLMs) to synthesize high-quality components, most approaches restrict the search to a single element—commonly a heuristic scoring function—thus missing broader opportunities for innovation. We introduce a broader formulation of solver design as a multi-strategy optimization problem, which seeks to jointly improve a set of interdependent components under a unified objective. To address this, we propose MOTIF—Multi-strategy Optimization via Turn-based Interactive Framework—a novel framework based on Monte Carlo Tree Search that facilitates turn-based optimization between two LLM agents. At each turn, an agent improves one component by leveraging the history of both its own and its opponent’s prior updates, promoting both competitive pressure and emergent cooperation. This structured interaction broadens the search landscape and encourages the discovery of diverse, high-performing solutions. Experiments across multiple COP domains show that MOTIF consistently outperforms state-of-the-art methods, highlighting the promise of turn-based, multi-agent prompting for fully automated solver design.

Sensor node deployment is a fundamental problem in IoT-enabled networks, as deployment quality directly affects sensing coverage, connectivity, and overall system performance in applications such as environmental monitoring, industrial IoT, and smart cities. In realistic deployment environments, the presence of obstacles and infeasible regions substantially complicates sensor placement, making coverage-oriented deployment a challenging non-convex and constraint-dominated optimization problem. In this paper, we investigate the obstacle-aware sensor node deployment problem, which aims to optimally place a fixed number of sensor nodes within a bounded two-dimensional region containing obstacles to maximize effective coverage of obstacle-free areas under feasibility and safety constraints. To address the resulting high-dimensional search space, we propose a hybrid GA-PSO algorithm with collective learning that integrates the global exploration capability of genetic algorithms with the fast convergence behavior of particle swarm optimization. The collective learning mechanism enables efficient information sharing among candidate solutions, improving convergence stability, solution quality, and resistance to premature stagnation. Extensive simulation results over diverse obstacle-rich deployment scenarios demonstrate that the proposed hybrid GA-PSO approach consistently achieves superior coverage performance, stronger robustness, and better scalability compared with representative baseline methods, confirming its effectiveness for coverage-oriented sensor deployment in complex IoT-enabled environments and large-scale deployments.

The Coordinated Truck-Drone Routing with Time Windows and Backhauls poses a complex challenge in collaborative logistics, requiring tight coordination between ground vehicles and drones under stringent operational constraints. Unlike traditional back-haul routing models that impose a strict priority between linehaul deliveries and backhaul pickups, this paper adopts an improved backhaul paradigm that allows pickups and deliveries to be interleaved along a route. Moreover, drones operate under limited endurance and can be launched and recovered by vehicles to serve selected customers, enabling flexible cooperation while preserving synchronization. We formulate this setting as a bi-objective problem that minimizes total cost and waiting time, and develop a Mixed-Integer Linear Programming model to optimally solve small-scale instances for benchmarking. For larger instances, this paper proposes the Constraint-Integrated Adaptive Grid-based Evolutionary Algorithm (CIAGEA) that integrates adaptive Pareto grid adjustment, adaptive local search, and a diversity mechanism to balance convergence and diversity while preserving feasibility. Extensive experiments on benchmark instances show that CIAGEA consistently outperforms state-of-the-art algorithms in terms of Hypervolume and Inverted Generational Distance, achieving particularly strong gains on large-scale problems and producing solutions close to optimal on small instances.

Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based Evolutionary Program Search (LLM-EPS). While previous LLM-EPS studies obtained great performance on various tasks, there is still a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation, which is a critical factor in large heuristic search spaces. In this study, we address this gap by proposing two diversity measurement metrics and perform an analysis on previous LLM-EPS approaches, including FunSearch, EoH, and ReEvo. Results on black-box AHD problems reveal that while EoH demonstrates higher diversity than FunSearch and ReEvo, its objective score is unstable. Conversely, ReEvo's reflection mechanism yields good objective scores but fails to optimize diversity effectively. With this finding in mind, we introduce HSEvo, an adaptive LLM-EPS framework that maintains a balance between diversity and convergence with a harmony search algorithm. Through experimentation, we find that HSEvo achieved high diversity indices and good objective scores while remaining cost-effective. These results underscore the importance of balancing exploration and exploitation and understanding heuristic search spaces in designing frameworks in LLM-EPS.

The dynamic delivery problem poses a complex challenge with many practical applications in logistics and transportation. Unlike the static delivery problem, where all order details are known, the dynamic delivery problem deals with continuously evolving information, with only partial data about orders available at any given moment. This paper presents the Multi-objective Dynamic Pickup and Delivery Problem with Time Windows framework, which integrates multiple objectives, including minimizing energy consumption, reducing waiting time, and ensuring fairness for both customers and vehicles. The goal is to lower overall system costs while balancing the customer experience and the workload of service providers. Previous research has primarily focused on optimizing a single objective or converting other objectives into constraints, which can limit the flexibility and effectiveness of the solutions. Our approach tackles this challenge by introducing a Pareto Front Grid-guided Multi-Objective Evolutionary Algorithm that incorporates two-sided fairness—ensuring equitable treatment for customers and service providers. The experimental results reveal that our method substantially outperforms existing multi-objective and single-objective algorithms specifically designed for the dynamic pickup and delivery problem on Hypervolume metric and Inverted Generational Distance metric.

Non-Matching Reference-based Speech Quality Assessment models typically require numerous references during inference to ensure stable and accurate predictions. However, this dependency introduces significant computational overhead, limiting their suitability for real-time applications. In this paper, we propose a novel training paradigm that directly addresses prediction instability at its source by integrating multiple references during training rather than during inference, as in existing approaches. This method allows the model to capture the inherent variability of reference signals, thereby enhancing prediction reliability. Additionally, we introduce an auxiliary variance loss function to minimize inconsistencies across predictions, ensuring stable assessments regardless of the number of references used. Experiments on the NISQA datasets demonstrate that, with the same training time, our method achieves consistent predictions with a single reference during inference, resulting in a 100-fold reduction in computational time while maintaining high accuracy.

Encrypted network traffic classification has become a critical task with the widespread adoption of protocols such as HTTPS and QUIC. Deep learning-based methods have proven to be effective in identifying traffic patterns, even within encrypted data streams. However, these methods face significant challenges when confronted with new applications that were not part of the original training set. To address this issue, knowledge transfer from existing models is often employed to accommodate novel applications. As the complexity of network traffic increases, particularly at higher protocol layers, the transferability of learned features diminishes due to domain discrepancies. Recent studies have explored Deep Adaptation Networks (DAN) as a solution, which extends deep convolutional neural networks to better adapt to target domains by mitigating these discrepancies. Despite its potential, the computational complexity of discrepancy metrics, such as Maximum Mean Discrepancy, limits DAN’s scalability, especially when applied to large datasets. In this paper, we propose a novel DAN architecture that incorporates Smooth Characteristic Functions (SCFs), specifically SCF-unNorm (Unnormalized SCF) and SCF-pInverse (Pseudo-inverse SCF). These functions are designed to enhance feature transferability in task-specific layers, effectively addressing the limitations posed by domain discrepancies and computational complexity. The proposed mechanism provides a means to efficiently handle situations with limited labeled data or entirely unlabeled data for new applications. The aim is to limit the target error by incorporating a domain discrepancy between the source and target distributions along with the source error. Two statistics classes, SCF-unNorm and SCF-pInverse, are used to minimize this domain discrepancy in traffic classification. The experimental results demonstrate that our proposed mechanism outperforms existing benchmarks in terms of accuracy, enabling real-time traffic classification in network systems. Specifically, we achieve up to 99% accuracy with an execution time of only three milliseconds in the considered scenarios.

In recent years, the Vehicle Routing Problem, with its variants handling complex realistic constraints, continues to be a key area of research, reflecting its enduring importance in modernized societies. Nevertheless, less attention has been paid to the variant with the flexibility of customer demands. This paper introduces the problem of addressing the integration of drones into the existing transportation system to maximize the profit of distributing freight during a limited working time by allowing flexibility in the delivered quantity. We propose a Mixed Integer Linear Programming (MILP) formulation and a genetic algorithm (GA) to solve the problem. To assess the proposed algorithm, we create the benchmark instances of the problem from published data sources. Experimental results on the instances show a significantly better performance of our proposal than MILP. Moreover, we evaluate our approach by comparing its performance with existing solutions in published closely related works.

Presently, the Internet infrastructure continues to be improved to satisfy the growing demands of users. Installing hardware devices in the network's server center faces many limitations, such as difficulties in device replacement, high investment costs, and operating expenses. The emergence of Network Function Virtualization (NFV) technology has addressed these drawbacks. This technology replaces dedicated hardware with software running in a virtualized environment, offering a more flexible and scalable solution. However, resource allocation is a critical concern in NFV networks. Proper resource allocation can enhance service quality and reduce network deployment costs. This paper investigates the network resource allocation problem to achieve two objectives: i) reduce network deployment costs and ii) decrease the latency of service chains in the network. We propose the modification of Non-dominated Sorting Genetic Algorithm 2, called NSGA2-H, to solve the multi-objective problem. The distinctive feature of the proposed algorithm, compared to the traditional NSGA2, is the use of a selective mutation operator. This mutation occurs on individuals at the first front, resembling a shift operation to enhance the algorithm's exploration capability. Experimental results on real-world datasets have demonstrated the effectiveness of the proposed algorithm.

This paper considers fairness designs under the user-centric framework with heterogeneous receivers comprising access points (APs) and a satellite. We exploit the closed-form ergodic throughput per user to formulate a generic optimization class that addresses the network fairness subject to the association patterns of all the users. Exhibiting the combinatorial structure, the global optimal solution to the association patterns can be obtained by an exhaustive search for small-scale networks with a small number of APs and users. For large-scale networks, we design two low computational complexity algorithms based on evolutionary computation to obtain a good solution in polynomial time. Specifically, we adapt the genetic algorithm (GA) to handle the discrete feasible region and the fairness metrics. Numerical results demonstrate that the optimized association patterns significantly improve the per-user throughput. The proposed GA-based algorithms yield the global optimum for small-scale networks coincided with an exhaustive search. Besides, the GA-based algorithms unveil practical association patterns for large-scale networks.

Neural Architecture Search (NAS) is a promising approach to automate the design of neural network architectures, which can find architectures that perform better than manually designed ones. Hardware-aware NAS is a real-world application of NAS where the architectures found also need to satisfy certain requirements for the deployment of specific devices. Despite the practical importance, hardware-aware NAS still receives a lack of attention from the community. Existing research mostly focuses on the search space with a limited number of architectures, reducing the search process to finding the optimal hyperparameters. In addition, the performance evaluation of found networks is resources-intensive, which can severely hinder reproducibility. In this work, we propose a genetic algorithm approach to the hardware-aware NAS problem, incorporating a latency filtering selection to guarantee the latency validity of candidate solutions. We also introduce an extended search space that can cover various existing architectures from previous research. To speed up the search process, we also present a method to estimate the latency of candidate networks and a training-free performance estimation method to quickly evaluate candidate networks. Our experiments demonstrate that our method achieves competitive performance with state-of-the-art networks while maintaining lower latency with less computation requirements for searching.

Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://airvlab.github.io/grasp-anything/.

Visual navigation, a foundational aspect of Embodied AI (E-AI) and robotics has been extensively studied in the past few years. While many 3D simulators have been introduced for the visual navigation tasks, scarcely works have combined human dynamics, creating the gap between simulation and real-world applications. Furthermore, current 3D simulators incorporating human dynamics have several limitations, particularly in terms of computational efficiency, which is a promise of modern simulators. To overcome these issues, we introduce HabiCrowd, the new standard benchmark for crowd-aware visual navigation that includes a crowd dynamics model with diverse human settings into photorealistic environments. Empirical evaluations demonstrate that our proposed human dynamics model achieves state-of-the-art performance in collision avoidance while exhibiting superior computational efficiency compared to its counterparts. We leverage HabiCrowd to conduct several comprehensive studies on crowd-aware visual navigation tasks and human-robot interactions. The source code and data can be found at https://habicrowd.github.io/.

As one of fundamental problems in robotics, coverage path planning (CPP) requires the robot path to cover the entire workspace which has been employed in several essential applications such as cleaning robots, land mine detector, lawnmowers and automated harvesters . Unlike most of existing studies considering the CPP problem under a unrealistic assumption of infinity energy, this paper takes the battery limitation of robots into account. This poses a significant challenge for enabling an efficient coverage path while satisfying the limited energy constraint, even in a priori known environment. Handling this challenge, we propose a BWave Framework that guides the robot to move following an improved Boustrophedon-like motion and a special area prioritization and especially, to return a charging station effectively before an exhausted energy. To that end, a weighted map is applied for recognizing the special areas, namely trap regions, and governing the robot to enter these fields in priority. Moreover, a return matrix, which forms the shortest-path tree from the charging station, is pre-computed to not only validate the energy requirement, but also speed up the calculation process of return and advance paths during the robot’s operation. We then evaluate BWave Framework extensively in various scenarios in both generated and real-life indoor maps datasets. The results show that compared to typical baseline methods , BWave Framework achieves the CPP solution at a significantly accelerated running time, namely 51.5 to 72.8 times lower for generated maps, and 44.8 to 255 times for real maps, while reducing the total path length by 2.4%–17.6% and by 2.9%–18.5%, respectively. Moreover, the proposed method also outperforms the baselines in terms of overlap rate, number of returns and accounts for a lower number of deadlocks .

With the appearance of more and more devices connected to the Internet, the world has witnessed an ever-growing number of data to be processed. Among those, many tasks require swift execution time, while the storage and computation capability of Internet of Things (IoT) devices are limited. To address the demands of delay-sensitive tasks, we present a vehicular edge–cloud computing (VECC) network that leverages powerful computation capabilities through the deployment of servers in proximity to task-generated devices, as well as the utilization of idle resources from smart vehicles to share the workload. Because these limited resources are vulnerable to sudden data arising, it is imperative to incorporate cloud servers to prevent system overload. The challenge now is to find a task offloading strategy that collaborates both edges and cloud resources to minimize the total time surpassing the quality baseline of each task (tolerance time) and make all tasks meet their soft deadlines of quality. To reach this goal, we first model the task offloading problem in VECC as a Markov decision process (MDP). Then, we propose advantage-oriented task offloading with a dueling actor-insulator network scheme to solve the problem. This value-based reinforcement learning (RL) method helps the agent find an effective policy when not knowing all the state attributes changes. The effectiveness of our method is demonstrated by performance evaluations based on real-world bus traces in Rio de Janeiro (Brazil). The experimental results show that our proposal reduces the tolerance time by at least 8.81% compared to other RL algorithms and 75% compared to greedy approaches.

In recent years, the utilization of fog cloud-based Internet of Things (IoT) applications has been steadily rising due to the exponential growth of data produced by interconnected smart devices. However, cloud providers who are responsible for these IoT applications face two critical problems: 1) how to protect the system from untrusted users and 2) how to allocate processing units to meet the demands with acceptable costs. The fog–cloud blockchain system (FCB), proposed in past research, provides a perfect solution for the former question by integrating Blockchain’s security qualities into the fog–cloud paradigm. In this article, we address the latter question by proposing an improved version of the life-choice-based optimization algorithm (ILCO) to solve the task scheduling for Bag-of-Task applications in the FCB system. Task scheduling is one of the most prominent problems in resource allocation. Our proposed algorithm not only increases the convergence speed but also maintains diversity better, optimizing the FCB’s power, latency, and cost. Under a single-objective problem setting, ILCO outperforms LCO and similar state-of-the-art methods by achieving better results for FCB’s latency and power consumption.

In this study, we investigate the trade-off between reliability and security in unmanned aerial vehicle (UAV) communications systems, considering a UAV-terrestrial network aided by a relay powered by a dedicated power beacon. For this system, we derive the outage probability (OP) under both exact and approximate frameworks and compute the approximations in the closed-form expressions. For the security aspect, we also derive the intercept probability (IP) under exact and approximated frameworks. To minimize the IP, a friendly jamming technique is employed whereby the power beacon constantly broadcasts artificial noise (AN) toward an eavesdropper. Based on the derived mathematical framework, we then formulate a bi-objective optimization problem by jointly minimizing the OP and IP with respect to the UAV’s position and the time-switching (TS) ratio. A suitable algorithm, named non-dominated sorting genetic algorithm version II (NSGA-II), is deployed to obtain the sub-optimal solution. Finally, numerical results are presented to verify the accuracy of the proposed mathematical framework and the superiority of jointly minimizing both the OP and IP using only the channel statistics.

This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we first derive the analytical downlink symbol error rate (SER) of each user as a multivariate function of both the phase-shift and beamforming vectors. The analytical SER enables us to obtain insights into the synergistic dynamics between the RIS and MIMO communication. We then introduce a novel average SER minimization problem subject to the practical constraints of the transmitted power budget and phase shift coefficients, which is NP-hard. By incorporating the differential evolution (DE) algorithm as a pivotal tool for optimizing the intricate active and passive beamforming variables in RIS-assisted communication systems, the non-convexity of the considered SER optimization problem can be effectively handled. Furthermore, an efficient local search is incorporated into the DE algorithm to overcome the local optimum, and hence offer low SER and high communication reliability. Monte Carlo simulations validate the analytical results and the proposed optimization framework, indicating that the joint active and passive beamforming design is superior to the other benchmarks.

Network function virtualization is a promising ar-chitecture for replacing dedicated hardware middle boxes with adaptable software, commonly called virtual network functions. A service function chain, which is made up of a set of ordered virtual network functions, can serve as the network function virtualization equivalent of a service. Allocating resources at servers is one of the most challenging problems in network function virtualization because of resource restrictions and the increasing number of services/requests. This paper considers the path planning for service function chain requests in network function virtualization to maximize the number of accepted requests. We formulate the problem as a Mixed Integer Linear Programming problem to find the optimal solution. However, the formulated Mixed Integer Linear Programming becomes complex to solve with increasing decision variables and constraints with increased network size. Since the problem is NP-hard, we propose the genetic programming-based algorithm to obtain near-optimal solutions to the problem efficiently. The links' bandwidth and servers' resources are considered during the service function chain routing. Simulation results show that the proposed solution is very close to the optimum and outperforms existing works concerning the service acceptance ratio.

Real-world continuous control problems often require optimizing for multiple conflicting objectives. Various works in multi-objective reinforcement learning have been conducted to tackle such issues and obtained impressive performance. At the same time, evolutionary algorithms (EAs), which are extensively used in multi-objective optimization, have recently been demonstrated their competitiveness to RL algorithms, including multi-objective control for environments with discrete action spaces. However, using EAs for multi-objective continuous robot control is still an under-explored topic. For the single-objective setting, the Proximal Distilled Evolutionary Reinforcement Learning (PDERL) framework succeeds in combining the robustness of EA and the efficiency of RL methods. In this work, we bring the strengths of PDERL to the multiobjective realm to create the novel multi-objective PDERL framework called MOPDERL that consists of a warm-up stage and an evolution stage. In particular, MOPDERL collaboratively optimizes policies for each separate objective in the warm-up stage, and then exchanges that knowledge for further policy improvement during the multi-objective evolution stage. We benchmark MOPDERL on six MuJoCo robot locomotion environments, which have been modified for the multi-objective context. The results show that MOPDERL produces better-quality Pareto fronts and higher metric scores than the state-of-the-art PGMORL algorithm across five out of six environments.

Finding dense subgraphs from a large graph is a fundamental graph mining task with many applications. The notion is recently formulated of locally densest subgraph (LDS) is recently formulated to identify multiple dense subgraphs that cover different regions of a large graph. Informally, an LDS is a subgraph with the highest density in its local region. The state-of-the-art algorithm for computing top-k LDSes with the highest densities is LDS. It iteratively computes the densest subgraph and removes it from the graph, where all the computed densest subgraphs form the candidates of LDSes. Then, each candidate is verified through a costly maximum flow computation. Although advanced pruning techniques are proposed in LDS, the verification step is still time consuming especially for not-so-small k values. In this paper, we aim to improve the efficiency of finding top-k LDSes by designing verification-free approaches. Our algorithms are based on our observation that the set of maximal λ-compact subgraphs for all possible λ values form a hierarchical structure, and LDSes are simply leaves in the hierarchical structure. Thus, we propose a divide-and-conquer algorithm LDS-DC as well as an optimized algorithm LDS-Opt to efficiently identify top-k LDSes without constructing the entire hierarchical structure. Both of our algorithms have lower time complexities than LDS. Extensive empirical studies on real graphs show that our optimized algorithm LDS-Opt outperforms LDS for all k values, and the improvement is up-to several orders of magnitude.

In the age of the ever-growing number of tasks generated from the Internet of Things (IoT) devices, one of the most crucial problems with enhancing the Quality of Service in multi-access computing (MEC) is to have a low overdue-task rate (tasks with processing time greater than their deadlines) while minimizing the energy consumed. To properly formulate the task offloading in a vehicular network, we consider both the number of overdue tasks and the total energy consumed by the edge system. We focus not only on maximizing the users' experience by minimizing the percentage of overdue tasks rate but also on saving energy for the service provider. Thus, our Deadline-constrained and Energy-aware problem requires finding a task offloading strategy to reduce the total power consumption of the edge system while still minimizing the number of overdue tasks. Findings from a 2D-street real-data bus traces are also provided for analysis. Furthermore, we develop a schema based on rein-forcement learning techniques named Deadline Constrained and Energy-Aware Task Offloading (DCEAO) to solve the problem. It proves to reduce the base station's power consumption by 38% to 51% while maintaining competitive overdue-task rate to other benchmarks that only focus on minimizing overdue-task rate.

Direction sensor networks are robust systems employed for detecting phenomena in environments or monitoring objects therein. They have a wide range of applications across many different industries and fields. In terms of the availability of resources, direction sensor networks deal with two problems: over-provision and under-provision of sensors. Over-provision occurs when there are too many sensors in the monitoring area, resulting in wasted resources and unnecessary energy consumption as some sensors are not well utilized. In contrast, under-provision occurs when there are too few sensors in the monitoring area, leading to the coverage of targets not satisfied. To ensure balanced coverage in under-provisioned environments, sensors must be placed so as to provide nearly equal fault tolerance to all objects, thereby enhancing the operational efficiency of the network. On the other hand, in over-provisioned environments, the number of active sensors needs to be minimized so that energy consumption is efficient. This study focuses on solving the Q-coverage problem in adjustable-orientation direction sensor networks, aiming to optimize a bi-level objective: maximizing network coverage balancing while minimizing sensor count in both under-provisioned and over-provisioned environments. The proposed Improved Genetic Algorithm utilizes novel operators, including Greedily-tuned Simulated Binary Crossover and Adaptive Polynomial Mutation. Evaluation parameters, including the Q-Balancing Index, Distance Index, Coverage Quality, Power Consumption, and the number of active sensors, demonstrate the efficiency of the proposed algorithm compared to other existing methods.

Current years have witnessed an increment in the number of research activities on improving the efficacy of multitasking algorithms for tackling challenging optimization problems. However, current approaches often present two potential problems. First, although tasks may have different characteristics, existing literature usually utilizes only one search operator for all of them. Second, while multitasking environments comprise tasks of varying difficulty, previous proposals treat them equally. This article proposes an algorithm named ensemble multifactorial evolution with biased skill-factor inheritance (EME-BI) for optimizing a large number of tasks simultaneously. In EME-BI, an effective parameter adaptation based on the knowledge transfer quality with biased skill-factor inheritance mechanism is designed to minimize negative transfer and allocate generated offspring to tasks that need resources. Besides, instead of using only one fixed search operator, EME-BI can automatically select the most appropriate one for each task at each evolutionary stage. Finally, the proposed algorithm is armed with a dynamically adjusted population size to promote exploitation. Empirical studies on various many-task benchmark problems and a real-world problem are conducted to verify the efficiency of EME-BI. The results portrayed that EME-BI achieves highly competitive performance compared to several state-of-the-art algorithms regarding the solution quality, convergence trend, and computation time. This proposal also won first prize at the CEC2021 Competition on Evolutionary Multitask Optimization, multitask single-objective optimization.

For the past few years, Hierarchical Path Computation Element (h-PCE) is an architecture that has been promoted to handle packet routing in multi-domain networks. However, this architecture has a potential drawback of poor scalability with respect to the number of domains. In tackling this complicated problem, we focuses on Inter-Domain Path Computation problem under the Domain Uniqueness constraint (IDPC-DU), which is employed to improve h-PCE. The objective of IDPC-DU is to find the shortest path between two given nodes that traverses every domain at most once. Since the IDPC-DU belongs to NP-Hard class, this paper introduces a two-level approach, combining the advantages of two metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Specifically, the upper-level GA plays the role of navigating the path for ants at the lower level ACO. Furthermore, an anti-stuck strategy that helps ants avoid being stuck is also equipped. To analyze the effectiveness of the proposed algorithm, experiments and comparisons with other algorithms are conducted. The results demonstrated that the proposed algorithm outperforms all other comnared ones in most cases.

While generative models achieve remarkable success in recent years, applying them to model tabular data is still challenging. The first problem of tabular data is the categorical encoding scheme, in which each categorical value is represented as a one-hot vector. It leads to the problem of very sparse high dimensional input space, especially when the cardinality of values of each attribute is high. This is problematic to GAN training since a trivial discriminator can simply distinguish real and fake data by checking the distributions sparseness. The second problem in tabular data is its hard constraint and discrete signals property which is challenging for neural networks. The modelling of discrete features is often associated with counting problem where gradient signals are not well-prepared for. As a result, current GAN methods might overlook and ignore these discrete features causing the mode-collapse in tabular data modelling. In this paper, we propose a unified framework to solve these two problems: (i) we propose to embed the raw data into the highlevel features and train GAN these features instead to avoid the trivial sparseness detection by the discriminator (ii) we propose the graphical-conditional vector to encourage GAN to learn to generate the structure information across multiple attributes. The experimental results show that our proposed methods perform much better than the current state-of-the-art method on most tabular benchmark datasets. Source code is available at: https://github.com/dungdinhanh/GDEGAN

This paper considers a variant of the Vehicle Routing Problem with Drones applied in the door-to-door sampling service system. Given a set of technicians and a fleet of drones departing from a medical center and working independently, the objective consists in designing feasible trips for technicians and drones such that the time from collection to arrival at the medical center of each customer’s test kits is not exceeded a limited duration and minimal makespan is achieved. A mixed-integer linear program (MILP) model is presented and solved to optimality with small instances due to the computational complexity of this problem. Thus, a tabu search approach is proposed. Experiments are then carried out to demonstrate the performance of the proposed algorithm and the advantage of integrating drones into the sampling service system.

Neural Architecture Search (NAS) has received much attraction from the research community in recent years. However, due to the massive amount of computational resources required, it is still infeasible to employ NAS into research and production in small labs and companies. Recent methods in NAS that aim to speed up the evaluation process are often limit themselves in other aspects, such as the search space that NAS operates on. In this work, we propose TF-GeneNAS, an evolution-based training-free NAS approach with a dynamic search space and search strategy based on Gene Expression Programming. We conduct experiments on three tasks in both Computer Vision and Natural Language Processing domains to demonstrates the effectiveness of our method. With only 3 CPU days of searching needed, TF-GeneNAS can find network architectures with better performance than previous evolution-based methods, which can require days of GPU resources, thus significantly lower the cost of searching. We also perform further studies to show the impact of our training-free estimation strategy on the NAS process. We hope that our promising results can encourage further research into more efficient evolution-based NAS methods.

Forensic systems have recently been studied to detect and prevent deepfakes abuses such as fake personas, frauds, misinformation, or harassment. At the same time, anti-forensic deepfakes are being investigated to understand the gaps in these detection systems and pave the way for improvement. In this paper, we investigate the threat of anti-forensic fake personas, where a fraudster creates a fake personal profile from multiple anti-forensic deepfake images portraying a single identity. To comprehensively study this threat model, we consider three approaches that an attacker may use to conduct such attacks, encompassing both white- and black-box scenarios. A range of defense strategies is then proposed with the aim to improve the robustness of current forensic systems against such threats. Experimental result shows that while the attacks can bypass current detection, our proposed defense approaches that consider the multi-image nature of a fake persona can effectively mitigate this threat by lowering the attack success rate.

Recent years have witnessed an increment in the number of network components communicating through many network scenarios such as multi-layer and multi-domain, and it may result in a negative impact on resource utilization. An urgent requirement arises for routing the packets most efficiently and economically in large multi-domain networks. In tackling this complicated area, we consider the Inter-Domain Path Computation problem under Node-defined Domain Uniqueness Constraint (IDPC-NDU), which intends to find the minimum routing cost path between two nodes that traverses every domain at most once. Owing to the NP-Hard property of the IDPC-NDU, applying metaheuristic algorithms to solve this problem usually proves more efficient. In like manner, this paper proposes a Two-level Genetic Algorithm (PGA), where the first level determines the order of the visited domains, and the second level finds the shortest path between the two given nodes. Furthermore, to facilitate the finding process, a method to minimize the search space and a new chromosome encoding that would reduce the chromosome length to the number of domains are integrated into this proposed algorithm. To evaluate the efficiency of the proposal, experiments on various instances were conducted. The results demonstrated that PGA outperforms other algorithms and gives results no more than twice the optimal values.

With the breakthrough of electromagnetic power transfer technology, wireless charging has emerged as a hopeful solution for the energy provisioning problem in wireless sensor networks. One of the prominent issues that affect the potential exploitation of this technology is the charging scheduling problem. However, existing works on this topic either focus mainly on using a single mobile charger for the whole network or suffer from several common limitations such as enforcing the chargers to visit all sensors or applying the rigid full-charging scheme. Moreover, they rarely delve into maximizing the survival nodes ratio, which impacts directly on the multi-hop communication of the network. This paper addresses the charging scheduling for multiple mobile chargers without the above limitations. We first formulate a maximum survival ratio problem and prove its NP-hardness. A charging scheme that exploits the advantages of the multifactorial evolutionary algorithm is then proposed to optimize the charging paths of all chargers simultaneously. We finally evaluate the efficacy of the proposed algorithm through extensive simulations. The experimental results demonstrate that our scheduling scheme provides promising outcomes in terms of survival ratio and the traveling energy of chargers.

In wireless sensor networks, the majority of data transmitted by sensor nodes is repeated over and over, and performing processes on them in many cases leads to increased power consumption and reduced network lifetime. Data aggregation is one of the techniques in reducing redundancy and improving energy efficiency; it also increases the lifespan of wireless sensor networks. In this paper, we address the issues of constructing the data aggregation tree that minimizes the total energy cost of data transmissions for two types of networks: without relay nodes and using relay nodes. Traditionally, evolutionary algorithms focus on constructing data aggregation trees for either without relay node networks or using relay nodes networks. Therefore, we propose Potential individuals based Multi-factorial Evolutionary Algorithm (P-MFEA) to solve both issues simultaneously. The proposed scheme shows improved performance in terms of energy consumption.

Wireless Rechargeable Sensor Network has emerged as a potential solution for the constrained energy problem in sensor networks in recent years. The charging process has been employed to prolong the sensor's lifetime. An effective charging algorithm requires simultaneously optimize the charging path and charging time at each charging location under the Mobile Charger's limited energy. The existing methods, however, are generally lacking in the literature. Moreover, most works are based on the assumption that Mobile Charger has sufficient or infinite energy to visit and charge all sensors within each charging cycle. This constraint leads to prolonging the waiting charging time of energy-hurry sensors and unnecessary visiting of energy-sufficient sensors. In this paper, we aim at minimizing energy depletion and the charging cost of Mobile Charger in Wireless Rechargeable Sensor Networks without the mentioned limitations above. We first mathematically formulate the investigated problem as mixed integer and linear programming. We propose a novel partial charging scheme based on fuzzy logic and genetic algorithms to determine which sensors should be charged in each cycle and optimize both charging paths and charging time simultaneously. A range of experimental simulations is conducted to demonstrate the effectiveness of our charging scheme. The simulation results show our proposed algorithm's effectiveness compared to the existing works concerning various performance metrics.

Inspired by the ability of human multitasking, there is a growing body of literature in the computational intelligence community dedicated to solving multiple problems concurrently. One of the research areas that have been receiving much attention in this topic is evolutionary multitasking, which is able to solve multiple complicated optimization problems together, yielding a better result than solving them in isolation. However, researches on evolutionary multitasking mostly focus on solving a small number of problems together. In an attempt to improve evolutionary multitasking, we propose Many-Task Multi-Armed Bandit Evolutionary Algorithm (Ma2BEA), including a new structure for the evolutionary multitasking algorithm. It also adopts a well-proven result of Multi-Armed Bandit (MAB) as the method to control the adaptive knowledge exchange between different tasks. In particular, the action of selecting which task to perform inter-task crossover is learned and decided by the designed MAB agent. We verify that Ma2BEA correctly learned the underlying relationship between tasks using the simple 10-task benchmarks. Besides, Ma2BEA is compared with other evolutionary many-tasking algorithms that have recently been proposed using the Single-Objective Many-task benchmark from the WCCI 2020 Competition on Evolutionary Multi-task Optimization. Empirical results show that Ma2BEA is competitive in terms of high solution quality and reasonable execution time.

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.

In the age of the ever-growing number of tasks being generated from IoT devices, one of the most crucial problems with enhancing the Quality of Service in multi-access computing is the system's limited resources. To this end, Vehicular Fog Computing (VFC) has emerged as a potential solution that utilizes the idle resources of vehicles to reduce the load imposed on the edge servers. In this paper, we leverage the advantages of both deep reinforcement learning and Fuzzy logic to propose Fuzzy Deep Q-learning base Offloading scheme (FDQO), a real-time offloading scheme in delay constrained VFC. Our objective is to maximize the Quality of Experiences (QoE), which indicates how the task meets its delay constraint. The experiment results show that our proposed approach significantly outperforms the existing algorithms. Specifically, FDQO improves the average QoE by 37.72% compared to using only Deep Q-learning, 7.47% compared to using only Fuzzy logic, and 19% compared to the ∊ -greedy strategy for multi-armed bandits.

Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Nowadays, connectivity among communication devices in networks has been playing a significant role, especially when the number of devices is increasing dramatically that requires network service providers to have a better architecture of management system. One of the popular approach is to divide those devices inside a network into different domains, in which the problem of minimizing path computation in general or Inter-Domain Path Computation under Domain Uniqueness constraint (IDPC-DU) problem in specific has received much attention from the research community. Since the IDPC-DU is NP-complete, an approximate approach is usually taken to tackle this problem when the dimensionality is high. Although Multifactorial Evolutionary Algorithm (MFEA) has emerged as an effective approximation algorithm to deal with various fields of problems, there are still some difficulties to apply directly MFEA to solve the IDPC-DU problem, i.e. different chromosomes may have different numbers of genes or to construct a feasible solution not violating the problem's constraint. Therefore, to overcome these limitations, MFEA algorithm with a new solution representation based on Priority-based Encoding is introduced. With the new representation of the solution, a chromosome consists of two parts: the first part encodes the priority of the vertex while the second part encodes information of edges in the solution. Besides, the paper also proposed a corresponding decoding method as well as novel crossover and mutation operators. Those evolutionary operators always produce valid solutions. For examining the efficiency of the proposed MFEA, experiments on a wide range of test sets of instances were implemented and the results pointed out the effectiveness of the proposed algorithm. Finally, the characteristics of the proposed algorithm are also indicated and carefully analyzed.

In recent years, Wireless Rechargeable Sensor Networks, which exploit wireless energy transfer technologies to address the energy constraint problem in traditional Wireless Sensor Networks, has emerged as a promising solution. There are two important factors that affect the performance of a charging process: charging path and charging time. In the literature, many studies have been done to propose efficient charging algorithms. However, most of the existing works focus only on optimizing the charging path. In this paper, we are the first one to jointly take into account both the charging path and charging time. Specifically, we aim at determining the optimal charging path and the charging time at each charging location to minimize the number of dead nodes. We first mathematically formulate the problem under mixed integer and linear programming. Then, we propose a periodic charging scheme, which is based on the Greedy and Genetic algorithm approaches. The experiment results show that our proposed the algorithm reduces significantly the number of dead nodes compared to a relevant benchmark.

Mobile edge computing (MEC) is a model that allows integration of computing power into telecommunications networks, to improve communication and data processing efficiency. In general, providing power to ensure the computing power of edge servers in the MEC network is very important. In many cases, ensuring continuous power supply to the system is not possible because servers are deployed in hard-to-reach areas such as outlying areas, forests, islands, etc. This is when renewable energy prevails as a viable source of power for ensuring stable operation. This paper addresses resource provisioning in the MEC network using renewable energy. We formulate the problem as a Markov Decision Problem and introduce a new approach to optimize this problem in terms of energy and time costs by using a reinforcement learning technique. Our simulation validates the efficacy of our algorithm, which results in a cost three times better than the other methods.

Connectivity is one of the most challenging issues in Wireless Sensor Network (WSN). Connectivity problems in WSN seek to guarantee a satisfactory communication capability where all mobile sensors can connect to a base station via relay nodes in all data gathering events. In this paper, we focus on minimizing the number of relay nodes while ensuring connectivity in Mobile Wireless Sensor Networks. We propose an improved heuristic algorithm named Clustered Steiner Tree Heuristic (CSTH) to solve this problem in two phases. The first phase is Node Anchoring, which utilizes a greedy approach to find anchor points among clusters of mobile sensors. The second phase is called Steiner Relay Placement, in which a Steiner tree-based heuristic is used to minimize the number of relay nodes while maintaining connectivity in each cluster. Experiments were performed to compare CSTH with previous state-of-the-art heuristics for the problem. Results show that our algorithm can significantly improve the number of required relay nodes as well as computation time.

Thanks to the advancements in wireless power transfer technologies, a new paradigm of wireless sensor network (WSNs) called wireless rechargeable sensor networks (WRSNs) has recently emerged. For a WRSN, designing an efficient charging schedule is a challenging issue due to the inherent constraints of WSNs. Although there have been many efforts to optimize the charging schedule, the existing works suffer from several critical problems. Firstly, they rarely tackle the dead node minimization problem, which is the ultimate objective of wireless charging. Secondly, most of the existing works assume impractical conditions, which include the unlimited battery capacity of the charger, and a fully charging scheme at the sensors. In this paper, aiming at minimizing the number of dead nodes, we propose a novel charging scheme based on the genetic algorithm. Our scheme works when the mobile charger has only limited capacity, and the sensors are charged partially at each charging round. The experiment results show that our proposed algorithm reduces the number of dead nodes significantly compared to other existing studies.

In recent years, multi-task optimization is one of the emerging topics among evolutionary computation researchers. Multi-Factorial Evolutionary Algorithm (MFEA) is developed based on that individuals, from various cultures, exchange their underlying similarities to improve the convergence characteristic. However, in terms of Multi-Objective Multi-Factorial Optimization (MOMFO), current algorithms employing nondominated front ranking and crowding distance still meet difficulties when the number of objective functions arises. In this paper, we propose a Muli-Objective Multi-Factorial Evolutionary Algorithm (MO-MFEA) with reference-point-based approach to improve the multitasking framework. Rather than using crowding distance to compute individual ranking in the context of MOMFO, we employ a set of reference points to determine the diversity of current population. On the other hand, we improve the guided method that automatically adapt the Random Mating Probability (RMP) in order to exploit shared knowledge among high similar task. Further improvement on genetic operators with JADE crossover and NSLS. The conducted experiments demonstrate our approach performs better than the baseline results.

Target coverage, connectivity, and fault tolerance are three challenging issues in wireless sensor networks. Target coverage aims to provide a sufficient monitoring quality where all targets in the surveillance region are covered by sensor nodes. Meanwhile, connectivity and fault tolerance seeks to guarantee a satisfactory communication capability where all sensors can connect to base station via relay nodes, while always able to find a backup path in case of failure. In this paper, we focus on minimizing the number of nodes (i.e., sensor nodes and relay nodes) while ensuring target coverage, connectivity and fault tolerance in wireless sensor networks. We approach this problem as two sub-problems. The first is Target Coverage, which requires placing sensor nodes to cover all targets. The second is Network Connectivity and Fault Tolerance, in which relay nodes need to be placed to connect sensor nodes to the base station, along with a backup path in case of failure. We propose an improved formulation of the Fault Tolerance constraint, as well as a new heuristic algorithm, MUTSP, which solves the first phase using a greedy approach, and the second phase with a spanning tree formulation. This method is compared and measured against previous state-of-the-art heuristics for the problem in our experiments. The results show that our algorithm can significantly improve the number of required nodes as well as computation time.

Randomized Greedy Algorithms (RGAs) are interesting approaches incorporating the random processes into the greedy algorithms to solve problems whose structures are not well understood as well as problems in combinatorial optimization. This paper introduces a new algorithm that combines the major features of RGAs and Shortest Path Tree Algorithm (SPTA) to deal with the Clustered Shortest-Path Tree Problem (CluSPT). In our algorithm, SPTA is used to determine the shortest path tree in each cluster while the combination between characteristics of the RGAs and search strategy of SPTA are used to construct the edges connecting clusters. To evaluate the performance of the algorithm, various types of Euclidean benchmarks are selected. The experimental results show the strengths of the proposed algorithm in comparison with some existing algorithms. We also analyze the influences of the parameters on the performance of the algorithm.

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

Increasing massive volume of data are generated every single second in this big data era. With big data from multiple sources, adversaries continuously mine private information for potential benefits. Motivated by this, we propose a generative adversarial net (GAN) driven noise generation method under the framework of differential privacy. We add one more perceptron, which is a specifically devised differential privacy identifier. After the generator produces the noise, the discriminator and the proposed identifier game with each other to derive the Nash Equilibrium. Extensive experimental results demonstrate the proposed model meets differential privacy constraints and upgrade data utility simultaneously.

Target coverage and connectivity are two main challenging and important issues in wireless sensor networks. The former is for providing sufficient monitoring quality where all points of interest in the network are covered by sensor nodes and the latter is for guaranteeing satisfactory communicating capability where all sensors can connect to at least one sink via relay nodes. In this paper, we focus on minimizing the number of nodes (i.e., sensor nodes and relay nodes) to provide target coverage and connectivity in wireless sensor networks with multiple sinks. We formulate the problem as two sub-problems. The first one (named as TC) is for placing sensor nodes to cover all targets and the second one (named as NC) is for placing relay nodes to connect sensor nodes to the sinks. We then propose a heuristic algorithm for the TC problem that exploits clustering technique. We also propose two heuristic algorithms for the NC problem that base on greedy approach and spanning tree. The experiment results show that our protocols can significantly reduce the number of required nodes in comparison with existing protocols.

Arising from the need of all time for optimization of irrigation systems, distribution network and cable network, the Cluster Shortest Path Tree Problem (CSTP) has been attracting a lot of attention and interest from the research community. For such an NP-Hard problem with a great dimensionality, the approximation approach is usually taken. Evolutionary Algorithms, based on biological evolution, has been proved to be effective in finding approximate solutions to problems of various fields. The multifactorial evolutionary algorithm (MFEA) is one of the most recently exploited realms of EAs and its performance in solving optimization problems has been very promising. The main difference between the MFEA and the traditional Genetic Algorithm (GA) is that the former can solve multiple tasks at the same time and take advantage of implicit genetic transfer in a multitasking problem, while the latter solves one problem and exploit one search space at a time. Considering these characteristics, this paper proposes a MFEA for CSTP tasks, together with novel genetic operators: population initialization, crossover, and mutation operators. Furthermore, a novel decoding scheme for deriving factorial solutions from the unified representation in the MFEA, which is the key factor to the performance of any variant of the MFEA, is also introduced in this paper. For examining the efficiency of the proposed techniques, experiments on a wide range of diverse sets of instances were implemented and the results showed that the proposed algorithms outperformed an existing heuristic algorithm for most of the testing cases. In the experimental results section, we also pointed out which cases allowed for a good performance of the proposed algorithm.

The wide range of applications of Cluster Tree Problems has been motivating extensive research into various algorithms and techniques with a view to promoting both efficiency of the solving and qualities of solutions. A representative of Cluster Tree Problems, the Cluster Shortest-Path Tree Problem (CSTP) arose from the practical need to optimize network systems such as irrigation systems, network cables and distribution systems. In this paper, we proposed the Multifactorial Evolutionary Algorithm (MFEA) to approach the CSTP with a representation scheme based on the Cayley Code. The proposed algorithm exploit advantages of Cayley Code for improving the MFEAs performance and quality solutions. This approach also applied new decoding method to transform the solution from the unified search space to the tasks. Experiments were conducted to compare the performances of the proposed to another approximation algorithm on various set of instances. The experimental results show that proposed algorithm surpass existing algorithm on almost test cases.

Recent years, the field of Multi-Objective Optimization (MOO) has attracted remarkable consideration among evolutionary computation researchers. Evolutionary multitasking paradigm within the domain of MOO has been proposed and demonstrated on some benchmark test functions that indicates potential applications in real world problems. The concept of evolutionary multi-tasking is founded on the fact that individuals from various cultures may share their underlying similarities, thereby facilitating improved convergence characteristics. However, the designate algorithm for MOO multi-tasking is originated from pure genetic search that means it does not imply any advanced local refinement method which also improves the rate of convergence. Memetic algorithms, which is known as a synergy of evolutionary with separate individual learning or local improvement procedures for problem search, offers converging to high-quality solutions more efficiently than their conventional evolutionary counterparts. Accordingly, in this paper, to excel MOO multi-tasking paradigm performance, we propose an algorithm which is based on the idea of Multi-Factorial Evolutionary Algorithm (MFEA) employing Guided differential evolutionary and Powell local search. The accomplished experimental results point out using memetic techniques does an impressive enhancement on Multi-objective continuous optimization.

Network Functions Virtualization (NFV) aims to support service providers to deploy various services in a more agile and cost-effective way. However, the softwarization and cloudification of network functions can result in severe congestion and low network performance. In this paper, we propose a solution to address this issue. We analyze and solve the online load balancing problem using multipath routing in NFV to optimize network performance in response to the dynamic changes of user demands. In particular, we first formulate the optimization problem of load balancing as a mixed integer linear program for achieving the optimal solution. We then develop the ORBIT algorithm that solves the online load balancing problem. The performance guarantee of ORBIT is analytically proved in comparison with the optimal offline solution. The experiment results on real-world datasets show that ORBIT performs very well for distributing traffic of each service demand across multipaths without knowledge of future demands, especially under high-load conditions.

In this paper, we propose a Genetic Algorithm (GA) for solving the problem of dimensional ordering in Radial Visualization (Radviz) systems. The Radviz is a non-linear projection of high-dimensional data set onto two dimensional space. The order of dimension anchors in the Radviz system is crucial for the visualization quality. We conducted experiments on five common data sets and compare the quality of solutions found by GA and those found by the other well-known methods. The experimental results show that the solutions found by GA for these tested data sets are very competitive having better cost values than almost all solutions found by other methods. This suggests that GA could be a good approach to solve the problem of dimensional ordering in Radviz.

This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature [28], which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We conducted extensive experiments on benchmark image datasets and compared the segmentation results with current proposed systems. The experimental results show that the performance of our system is comparable to, or even outperforms, those state-of-the-art algorithms. This is a promising approach as in this empirical study we used only texture-layout filter responses as feature and a basic setting of genetic algorithm. The framework is simple and can be extended and improved for many learning problems.

Given a connected, weighted, undirected graph G=(V, E) and a bound D, bounded diameter minimum spanning tree problem (BDMST) seeks spanning tree on G with smallest weight in which no path between two vertices contains more than D edges. This problem is NP-hard for 4 les D les |V| - 1. This paper proposes three new multi-parent recombination operators using different methods to choose parents in genetic algorithm for solving bounded diameter minimum spanning tree problem. Results of computational experiments are reported to show the efficiency of proposed algorithms.

In this paper, we propose a new heuristic, called Center-Based Recursive Clustering - CBRC, for solving the bounded diameter minimum spanning tree (BDMST) problem. Our proposed hybrid genetic algorithm [12] is also extended to include the new heuristic and a multi-parent crossover operator. We test the new heuristic and genetic algorithm on two sets of benchmark problem instances for the Euclidean and Non-Euclidean cases. Experimental results show the effectiveness of the proposed heuristic and genetic algorithm.

In this paper, a new hybrid genetic algorithm - known as HGA - is proposed for solving the Bounded Diameter Minimum Spanning Tree (BDMST) problem. We experiment with HGA on two sets of benchmark problem instances, both Euclidean and Non-Euclidean. On the Euclidean problem instances, HGA is shown to outperform the previous best two Genetic Algorithms (GAs) reported in the BDMST literature, while on the Non-Euclidean problem instance, HGA performs comparably with these two GAs.