The efficiency of Intelligent Transport Systems (ITS) runs on high-quality traffic data, however in real-world deployments, sensors failures, communication interruptions or other issues often lead to missing data, which affects the performance of ITS. Aiming at traffic data’s complex spatio-temporal characteristics, although the latent factorization of tensors (LFT) model has been widely used for missing-value completion, its non-convex objective function makes it difficult for first-order optimization methods to approximate high-quality second-order stationary points, therefore limiting the improvement of the completion accuracy. To address the issues, this paper proposes an incomplete tensor complementation model combining Tucker decomposition and second-order optimization strategy to improve the complementation accuracy and convergence stability. To address the issues, this paper proposes a Second-order Latent Factorization of Tensors based on Tucker Decomposition (SLTD), and efficiently solves it via Gauss-Newton approximation, so that it can significantly improve the model performance while keeping the computational cost low. Experimental results on real traffic datasets (in terms of average vehicle speed) from four cities verify the effectiveness of SLTD. Results show that the proposed model outperforms existing prevailing methods in terms of accuracy and provides a better solution for traffic data completion.

The frame-based method is not suitable for counting repetitive actions in event stream, since the framing process will disrupt the temporal information of events. For accurate count of repetitive actions in events, we propose a framework based on threefold ideas: a) converting event stream into time series, b) searching candidates of repetitive actions based on the ascending and descending trends of event time series, and c) checking the candidates with a fast dynamic time warping based method. For accurate counting repetitive actions, an action enhancement method for event time series and a Mann-Kendall test incorporated dynamic candidate selection algorithm are innovatively proposed. The experimental results on artificially synthesized and normally recorded event datasets demonstrate that our framework can count repetitive actions in event stream with high accuracy. All codes, datasets and examples of visualization can be found at https://github.com/ZYL618/action_count_in_events3.

Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.

Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions data is a hot yet thorny issue. An efficient approach to it is latent factor analysis (LFA), which commonly depends on large-scale non-convex optimization. Hence, it is vital to implement an LFA model able to approximate second-order stationary points efficiently for enhancing its representation learning ability. However, existing second-order LFA models suffer from high computational cost, which significantly reduces its practicability. To address this issue, this paper presents a Momentum-accelerated Hessian-vector algorithm (MH) for precise and efficient LFA on HiDS data. Its main ideas are two-fold: a) adopting the principle of a Hessian-vector-product-based method to utilize the second-order information without manipulating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning scheme for accelerating its convergence rate to a stationary point. Experimental results on nine industrial datasets demonstrate that compared with state-of-the-art LFA models, an MH-based LFA model achieves gains in both accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with the algorithms, e.g., a second-order algorithm, which implicitly rely on gradients.

Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. To perform artificial intelligence (AI)-assisted OP staging via chest X-ray image representational learning and classification commonly adopted to address it, where a Convolutional Neural Network (CNN) has proven to be efficient. However, unlike commonly encountered image classification tasks, OP staging relies heavily on the profusion level of opacities. The opacities in chest X-ray overlap with other tissues in the lung area and are hard to be represented by a standard CNN, thereby leading to inaccurate staging results. Aiming at implementing accurate determination of the opacities caused by pneumoconiosis, this study incorporates a dark channel prior method into a bi-branch learning structure, thereby establishing a Bi-branch Dark Channel Differential Convolutional Neural Network (BDCNN) for accurate AI-assisted OP staging. Its ideas are two-fold: a) extracting opacities caused by pneumoconiosis from chest X-ray with a dark channel prior-based dehazing method, and b) realizing multiple feature fusion via a bi-branch structure to ensure high staging accuracy. Experimental results on six real OP data cases demonstrate that the proposed BDCNN outperforms state-of-the-art models in obtaining accurate staging results for occupational pneumoconiosis.

Latent Factor (LF) models are highly effective in representing high-dimensional and incomplete (HDI) matrices. Hessian free (HF) optimization is an efficiency second-order algorithm to minimize the object function in LF models. An HF-based second-order LF model can achieve better accuracy representation results than first-order ones with affordable computational burden. However, its low rank representation ability relies on a more complex training process, multiple hyper-parameters work cooperatively to decide the results. Thus, these hyper-parameters should be turned with care since they are mutually influenced. The heavy hyper-parameters turning work reduces the practicability of a second-order LF model. To address this issue, this study incorporates the principle of particle swarm optimization into the second-order LF model to propose an adaptive second-order LF (ASLF) model. Experimental results on three HDI matrices reveal that ASLF model can be fine-tuned adaptively with acceptable computation burden.

With its potential to solve the problem of car parking difficulties, smart parking management systems have elicited a significant amount of interest from both academia and industry. Robust and reliable parking detection is a key technology for smart parking management systems. Presently, magnetic sensors are being widely used in parking detection. However, they are prone to produce high false and missed detection rates in environments that have geomagnetic interference. This article proposes a new sensing device based on the combination of a magnetic sensor and a pulsed coherent radar. The sensing device is used for parking detection in the context of Internet of Things. A Dempster–Shafer evidence theory-based collaborative fusion approach is developed to improve the accuracy of vehicle detection. Pulsed coherent radar is triggered by the magnetic sensor to reduce sampling power consumption. The prototype system is tested under various interference conditions. The results show that the combination of magnetic and radar sensors improves vehicle detection accuracy, and the lifetime of battery-powered sensor node is longer than five years.

Nearest neighbor query processing is a fundamental problem that arises in many fields such as spatial databases and machine learning. This article aims to address the Secure Nearest Neighbor (SNN) problem in cloud computing. Prior SNN schemes are both insecure and inefficient. In this article, we formally prove and experimentally demonstrate that the SNN scheme ASPE is actually insecure against even ciphertext only attacks. Although prior work proved that it is impossible to construct an SNN scheme even in much relaxed standard security models, we point out the flaws of the hardness proof. We propose an SNN scheme and prove that it is secure against adaptive chosen keyword attacks. Our scheme is efficient as its query processing complexity is logarithmic. To evaluate the efficiency of our SNN scheme, we implemented our scheme in C++ and compared its performance with a plain text scheme, binary scheme, and a PIR scheme on a large set of over 10 million real-world data points. Experimental results show that our scheme is fast (0.124 millisecond per query when data set size is 10 million) and scalable in terms of the number of data points.

With the rapid development of the insurance industry, more diverse insurance products are produced for consumers. Insurance portfolio problems have received increasing attention. While most studies focus on insurance portfolio problem for a single insured, insurance portfolio problems for a specific group of insured are even more intricate but little attention has been paid to. In this article, we propose a group insurance portfolio model for investment allocation of several insurance policies so that the total payout of the whole group can be maximized. The statistical average value of each parameter is considered in the model to approximate the expectation payout of the group insurance portfolio problem. To solve this problem, a coevolutionary estimation of distribution algorithm (EDA) utilizing the divide-and-conquer strategy is proposed. First, as the payout of each insured under a certain portfolio plan can be calculated separately, the proposed approach decomposes the group insurance portfolio problem into several single-insured insurance portfolio problems. In this way, the dimension of the optimization problem becomes lower compared to the original problem. An adaptive EDA is proposed to optimize the portfolio plan of each insured independently. Second, the group insurance portfolio problem remains a nonseparable problem since the investment amount of each insured is limited by the total investable amount of the whole group. A particle swarm optimization algorithm is adopted to cooperate with the EDA to optimize the proportion of allocation to each insured. The proposed algorithm is verified on various scenarios. The experimental results validate that the proposed approach is effective for the group insurance portfolio problem.

Feature matching is used to match features in the model image to their correspondences in the test image. Hypergraph matching makes use of the relationship among multiple features to improve matching accuracy, and existing algorithms usually achieve this aim by maximizing the matching score between features. In this paper we transform hypergraph matching of features to hypergraph clustering of candidate matches, which is then solved in the scenario of a multi-player clustering game. Noticing that the number of matches generated by this algorithm is usually small, we discuss the reason and present a density based group expansion method to increase the number of matches. Furthermore, we enforce the one-to-one constraint to maintain a high matching accuracy. Experiments on three real datasets show that our algorithm is able to generate a large number of matches efficiently without degrading the matching accuracy evidently.

Protein-protein interactions (PPIs) play a critical role in cellular activities. However, discover them experimentally is exhausted. How to predict the missing PPIs with the known PPI networks (PPINs) is a challenging task considering their large scale and extreme sparsity. To utilize the known data efficiently, this work proposes a Highly-Confident Protein Interactome Prediction (HPIP) model with two-fold ideas: a) employing a variational autoencoder model based on Gaussian distribution as a basic PPI predictor for processing the sparse input PPIN; b) embedding the basic PPI predictor into an elastic architecture which based on a block and probability summing strategy. Experimental results on two real world PPINs from the STRING database indicate that HPIP can effectively predict PPIs with high confidence.

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth in this algorithm is not adjustable, resulting in frequent training fluctuation or poor model convergence caused by overshooting. To address this issue, this study carefully investigates the connections between the performance of an NLFT model and its learning depth via SLF-NMUT to present a joint learning-depth-adjusting scheme for it. Based on this scheme, a Depth-adjusted Multiplicative Update on tensor algorithm is innovatively proposed, thereby achieving a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Empirical studies on two industrial data sets demonstrate that compared with the state-of-the-art NLFT models, a DNL model achieves significant accuracy gain when performing missing data estimation on a high-dimensional and incomplete tensor with high efficiency.Note to Practitioners—Multichannel data are often encountered in various big-data-related applications. It is vital for a data analyzer to correctly capture the temporal patterns hidden in them for efficient knowledge acquisition and representation. This article focuses on analyzing temporal QoS data, which is a representative kind of multichannel data. To correctly extract their temporal patterns, an analyzer should correctly describe their nonnegativity. Such a purpose can be achieved by building a nonnegative latent factorization of tensors (NLFT) model relying on a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. But its learning depth is not adjustable, making an NLFT model frequently suffer from severe fluctuations in its training error or even fail to converge. To address this issue, this study carefully investigates the learning rules for an NLFT model’s decision parameters using an SLF-NMUT and proposes a joint learning-depth-adjusting scheme. This scheme manipulates the multiplicative terms in SLF-NMUT-based learning rules linearly and exponentially, thereby making the learning depth adjustable. Based on it, this study builds a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Compared with the existing NLFT models, a DNL model better represents multichannel data. It meets industrial needs well and can be used to achieve high performance in data analysis tasks like temporal-aware missing data estimation

Evacuation path optimization (EPO) is a crucial problem in crowd and disaster management. With the consideration of dynamic evacuee velocity, the EPO problem becomes nondeterministic polynomial-time hard (NP-Hard). Furthermore, since not only one single evacuation path but multiple mutually restricted paths should be found, the crowd evacuation problem becomes even challenging in both solution spatial encoding and optimal solution searching. To address the above challenges, this article puts forward an ant colony evacuation planner (ACEP) with a novel solution construction strategy and an incremental flow assignment (IFA) method. First, different from the traditional ant algorithms, where each ant builds a complete solution independently, ACEP uses the entire colony of ants to simulate the behavior of the crowd during evacuation. In this way, the colony of ants works cooperatively to find a set of evacuation paths simultaneously and thus multiple evacuation paths can be found effectively. Second, in order to reduce the execution time of ACEP, an IFA method is introduced, in which fractions of evacuees are assigned step by step, to imitate the group-based evacuation process in the real world so that the efficiency of ACEP can be further improved. Numerical experiments are conducted on a set of networks with different sizes. The experimental results demonstrate that ACEP is promising.

The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.

The introduction of workflow in cloud computing has afforded a new and efficient way to tackle large-scale applications. As an NP-hard problem, how to schedule cloud workflows effectively and economically with deadline constraints and different kinds of tasks and resources is extraordinarily challenging. To solve this constrained problem, this paper intends to develop an intelligent scheduling system from the perspective of users to reduce expenditure of workflow, subject to the deadline and other execution constraints. A new estimation model of the task execution time is designed according to virtual machine settings in real public clouds and execution data from practical workflows. Based on the new model, an adaptive ant colony optimization algorithm is proposed to meet the quality of service and orchestrate tasks. The adaptiveness of the algorithm is embodied in two aspects. First, an adaptive solution construction method is designed that each solution is built with a dynamically changing resource pool, thus the search space of the algorithm is narrowed down and the execution time is decreased. Second, two heuristics with self-adaptive weight are introduced to adaptively meet different deadline settings. Simulating results on four types of workflows show that the proposed approach is effective and competitive.

High-dimensional and sparse (HiDS) matrices generated by recommender systems contain rich knowledge regarding various desired patterns like users' potential preferences and community tendency. Latent factor (LF) analysis proves to be highly efficient in extracting such knowledge from an HiDS matrix efficiently. Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model. However, current LF models mostly adopt a standard SGD algorithm. Can SGD be extended from various aspects in order to improve the resultant models' convergence rate and prediction accuracy for missing data? Are such SGD extensions compatible with an LF model? To answer them, this paper carefully investigates eight extended SGD algorithms to propose eight novel LF models. Experimental results on two HiDS matrices generated by real recommender systems show that compared with an LF model with a standard SGD algorithm, an LF model with extended ones can achieve: 1) higher prediction accuracy for missing data; 2) faster convergence rate; and 3) model diversity.

Virtual network embedding (VNE) is an important problem in network virtualization for the flexible sharing of network resources. While most existing studies focus on centralized embedding for VNE, distributed embedding is considered more scalable and suitable for large-scale scenarios, but how virtual resources can be mapped to substrate resources effectively and efficiently remains a challenging issue. In this paper, we devise a distributed VNE system with historical archives (HAs) and metaheuristic approaches. First, we introduce metaheuristic approaches to each delegation of the distributed embedding system as the optimizer for VNE. Compared to the heuristic-based greedy algorithms used in existing distributed embedding approaches, which are prone to be trapped in local optima, metaheuristic approaches can provide better embedding performance for these distributed delegations. Second, an archive-based strategy is also introduced in the distributed embedding system to assist the metaheuristic algorithms. The archives are used to record the up-to-date information of frequently repeated tasks. By utilizing such archives as historical memory, metaheuristic algorithms can further improve embedding performance for frequently repeated tasks. Following this idea, we incorporate the set-based particle swarm optimization (PSO) as the optimizer and propose the distributed VNE system with HAs and set-based PSO (HA-VNE-PSO) system to solve the VNE problem in a distributed way. HA-VNE-PSO is empirically validated in scenarios of different scales. The experimental results verify that HA-VNE-PSO can scale well with respect to substrate networks, and the HA strategy is indeed effective in different scenarios.

Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.

Wireless magnetic sensor networks (WMSNs) provide a low-power, low-cost solution for traffic information acquisition. Systems used for traffic speed estimation based on WMSNs have been well studied only when the geomagnetic field has not interfered. However, the widely developed electric rail transit systems, such as subways, light rails, and high-speed rail systems, produce magnetic field radiation that interferes with the geomagnetic field. So it is imperative to filter out noise in order to increase the detection accuracy. This article proposes a system for traffic speed estimation which can effectively eliminate the geomagnetic background interference. A morphological filter is designed for removing the interference and extracting the magnetic signatures of vehicles. The speed estimation is implemented based on the signatures reidentification. Experimental results verify that the proposed system is more accurate than existing systems.

Wireless magnetic sensor networks (WMSNs) offer an effective and efficient low-cost alternative for real-time parking space monitoring. Current algorithms used for parking space detection based on the WMSNs have been well studied but not without eliminating the geomagnetic field interference. Electric railway systems, such as subways and light rail systems, produce magnetic field radiation that interferes with the geomagnetic field. The electric railway magnetic field interference has increased and is prominent in urban settings; therefore, it is imperative to filter out the interference in order to increase the detection accuracy. This paper proposes an algorithm for parking space detection, which can effectively eliminate the geomagnetic background interference. We solve the parking detection problem through signal filtering technology. Based on mathematical morphology, we designed two filters for extracting morphological structures from interfered magnetic signals. The experimental results verify that the proposed algorithm is more accurate than the existing algorithms.

Nearest neighbor query processing is a fundamental problem that arises in many fields such as spatial databases and machine learning. ASPE, which uses invertible matrices to encrypt data, is a widely adopted Secure Nearest Neighbor (SNN) query scheme. Encrypting data by matrices is actually a linear combination of the multiple dimensions of the data, which is completely consistent with the relationship between the source signals and observed signals in the signal processing. By viewing dimensions of the data and the encrypted data as source signals and observed signals, respectively, we formally prove and experimentally demonstrate that ASPE is actually insecure against even ciphertext only attacks, using signal processing theory. Prior work proved that it is impossible to construct an SNN scheme even in much relaxed standard security models, we invalidate this hardness understanding by pointing out the incorrectness of the hardness proof.

Motivated by the fair rate allocation in a multiaccess Gaussian channel, this paper studies the problem of fair rate allocation over a generalized symmetric polymatroid with box constraints. The best-known algorithm for this problem has time complexity O(n5lnO(1)n). In this paper, we present a divide-and-conquer algorithm for this problem with quadratic running time. It is an implementation of a refined decomposing method for the more general separate concave maximization over a polymatroid with box constraints. A key ingredient of the algorithm is a linear-time algorithm for a generalized knapsack problem.

The rate capacity region of an uplink Gaussian channel is a generalized symmetric polymatroid. Practical applications impose additional lower and upper bounds on the rate allocations, which are represented by box constraints. A fundamental scheduling problem over an uplink Gaussian channel is to seek a rate allocation maximizing the weighted sum-rate (MWSR) subject to the box constraints. The best-known algorithm for this problem has time complexity O (n5 lnO(1) n). In this paper, we take a polymatroidal approach to developing a quadratic-time greedy algorithm and a linearithmic-time divide-and-conquer algorithm. A key ingredient of these two algorithms is a linear-time algorithm for minimizing the difference between a generalized symmetric rank function and a modular function after a linearithmic-time ordering.

Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

This paper develops a decomposition-based coevolutionary algorithm for many-objective optimization, which evolves a number of subpopulations in parallel for approaching the set of Pareto optimal solutions. The many-objective problem is decomposed into a number of subproblems using a set of well-distributed weight vectors. Accordingly, each subpopulation of the algorithm is associated with a weight vector and is responsible for solving the corresponding subproblem. The exploration ability of the algorithm is improved by using a mating pool that collects elite individuals from the cooperative subpopulations for breeding the offspring. In the subsequent environmental selection, the top-ranked individuals in each subpopulation, which are appraised by aggregation functions, survive for the next iteration. Two new aggregation functions with distinct characteristics are designed in this paper to enhance the population diversity and accelerate the convergence speed. The proposed algorithm is compared with several state-of-the-art many-objective evolutionary algorithms on a large number of benchmark instances, as well as on a real-world design problem. Experimental results show that the proposed algorithm is very competitive.

Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has been successfully employed by many evolutionary algorithms (EAs) to solve large-scale optimization problems. In practice, it is common that different subcomponents of a large-scale problem have imbalanced contributions to the global fitness. Thus, how to utilize such imbalance and concentrate efforts on optimizing important subcomponents becomes an important issue for improving performance of cooperative co-EA, especially in distributed computing environment. In this paper, we propose a two-layer distributed CC (dCC) architecture with adaptive computing resource allocation for large-scale optimization. The first layer is the dCC model which takes charge of calculating the importance of subcomponents and accordingly allocating resources. An effective allocating algorithm is designed which can adaptively allocate computing resources based on a periodic contribution calculating method. The second layer is the pool model which takes charge of making fully utilization of imbalanced resource allocation. Within this layer, two different conformance policies are designed to help optimizers use the assigned computing resources efficiently. Empirical studies show that the two conformance policies and the computing resource allocation algorithm are effective, and the proposed distributed architecture possesses high scalability and efficiency.

Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representative-based estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.

Cross-docks serve as distribution centers where shipments from different vendors are first consolidated according to their destinations, and then delivered to the retailers directly, with little or no storage in between. A critical problem encountered in the operation of cross-docks is the assignment of receiving and shipping doors, which greatly influences the labor or machinery cost of transferring the shipments between inbound and outbound transports. We show that the cross-dock door assignment problem (CDAP) is strictly non-deterministic polynomial-time complete. Although some deterministic algorithms have been reported to handle small-scale problems, the solutions to the middle- and large-scale CDAPs progressed at a slow pace. In this paper, we develop a nature-inspired dual-colony ant algorithm for CDAP, in which the two colonies of ants cooperatively search the optimal assignments of receiving and shipping doors to minimize the transferring costs of shipments. A collaborative local search strategy is designed and incorporated into the algorithm to enhance the search efficiency. Experiments have been conducted on a number of problem instances with different cross-dock sizes and freight flow patterns. The results show that the proposed algorithm is very competitive and can provide better solutions than the state-of-the-art heuristic algorithms.

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants.

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems for optimization. There are two general goals for solving MMOPs. One is to maintain population diversity so as to locate global optima as many as possible, while the other is to increase the accuracy of the solutions found. To achieve these two goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) is proposed in this paper. The novelties and advantages of DSDE include the following three aspects. First, a dual-strategy mutation scheme is designed to balance exploration and exploitation in generating offspring. Second, an adaptive selection mechanism based on APC is proposed to choose diverse individuals from different optimal regions for locating as many peaks as possible. Third, an archive technique is applied to detect and protect stagnated and converged individuals. These individuals are stored in the archive to preserve the found promising solutions and are reinitialized for exploring more new areas. The experimental results show that the proposed DSDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms when evaluated on the benchmark problems from CEC2013, in terms of locating more global optima, obtaining higher accuracy solution, and converging with faster speed.

With the rapid development of e-commerce, logistics industry becomes a crucial component in the e-commercial ecological chain. Impelled by both economical and environmental benefit, logistics companies demand automated tools more urgently than ever. In this paper, a dynamic logistic dispatching system is proposed. The underlying model of the dispatching system is the dynamic vehicle routing problem which allows new orders being received as the working day progress. With this feature, the system becomes more practical than the systems with traditional static vehicle routing models, but is also more challenging as the vehicles must be scheduled in a dynamic way. The core of the system is a specially designed set-based particle swarm optimization algorithm. According to the characteristic of the problem, a new encoding scheme is defined by set and possibility, and a local refinement method is designed to accelerate the convergence speed of the algorithm. In addition, two more techniques: 1) region partition and 2) archive strategy are incorporated in the dispatching system to reduce the complexity of the problem and to facilitate the optimization process, helping the dispatcher control the vehicles in real time. The proposed system is tested on various benchmarks with different scales. Experimental results show that the proposed dispatching system is effective.

Fractional Link scheduling is one of the most fundamental problems in wireless networks. The prevailing approach for shortest fractional link scheduling is based on a reduction to the maximum-weighted independent set problem, which itself may not admit efficient approximation algorithms. In addition, except for the wireless networks under the protocol interference model, none of the existing scheduling algorithms can produce a link schedule with explicit upper bounds on its length in terms of the link demands. As the result, the polynomial approximate capacity regions in these networks remain blank. This paper develops a purely combinatorial paradigm for fractional link scheduling in wireless networks. In addition to the superior efficiency, it is able to provide explicit upper bounds on the lengths of the produced link schedule. By exploiting these upper bounds, polynomial approximate capacity regions are derived. The effectiveness of this new paradigm is demonstrated by its applications in wireless networks under the physical interference model and wireless MIMO networks under the protocol interference model.

Consider a set of point-to-point communication requests in a multi-channel multihop wireless network, each of which is associated with a traffic demand of at most one unit of transmission time, and a weight representing the utility if its demand is fully met. A subset of requests is said to be schedulable without spectral splitting if they can be scheduled within one unit of time subject to the constraint each request is assigned with a unique channel throughout its transmission. This paper develops efficient and provably good approximation algorithms for finding a maximum-weighted subset of communication requests schedulable without spectral splitting.

In this study, secure communication in cognitive radio networks is investigated. A cooperative jammer is utilised to transmit artificial noise for interfering with the eavesdropper. The authors’ objective is to maximise the available secrecy rate of the secondary user (SU) under the interference power constraint at the primary user and the global transmit power constraint at the transmitters. In that case, beamforming vectors of the SU transmitter and the cooperative jammer, and power allocation between them need to be optimised. However, the optimisation problem is non‐convex and hard to solve. In order to tackle it, two suboptimal algorithms are proposed: complete orthogonal projection and partial orthogonal projection. In these algorithms, the original optimisation problems are first decoupled into two sub‐problems, which deal with the transmit beamforming vectors and power allocation, respectively. Then, the optimal solution is obtained by iteration alternately. Finally, simulation results verify the effectiveness of the proposed algorithms.

In this paper, we investigate the potential throughput gain brought by cooperative interference alignment (IA) in multi-hop MIMO networks. Unlike the fully-connected K-user interference network where IA is usually considered, IA cooperations in multi-hop MIMO networks are limited by node density, leading to an inevitable coexistence of IA and non-IA links. Though cooperating links within the same IA group can nullify intra-group interference via IA, uncoordinated inter-group interferences as well as the interferences from non-IA links retain with noises. Hence, whether or not IA can brought throughput gain for such networks is still an open question. In allusion to this question, we firstly extend a cross-layer PHY-MAC model for MIMO networks to jointly abstract the IA and non-IA links with a set of transmission patterns. Then we formulate the problem of throughput-optimal topology control for multi-hop MIMO networks with IA and that without IA and prove their NP-hardness. We further propose integer linear mathematical models for the formulated problems and investigate the benefits of IA cooperations via simulations. Simulation results show that regardless of the number of antennas, IA cooperations do not offer noticeable throughput gain in random networks.

In this paper, we revisit the minimum-length link scheduling problem (MLSP) in wireless networks. Unlike previous scheduling solutions that adopt the interference avoidance approach, we embrace interference among neighboring links via a new technique of interference alignment (IA). Specifically, we use IA to cancel the interference among cooperative neighboring links, while controlling uncoordinated interference from non-neighboring links by traditional link scheduling. This novel approach of scheduling is modeled as an integer linear program based on an extended multiple-input and multiple-output (MIMO)-Pipe model, which not only captures the complex interference relationship between links in multi-user MIMO networks with IA, but also provides a realistic understanding of the interaction of achievable rate and suffered interference both for IA links and non-IA links in multi-user MIMO networks. By resolving the formulated programs, we further investigate the effect of node density and antenna number on the performance gain brought by IA cooperations. Simulation results show that the benefits of IA in MLSP problem generally increase with node density, but decrease with the number of antennas.