As the scale of multiprocessor systems increases, so do the requirements for system reliability and security. Modeling how processors and links are connected in a multiprocessor system through a network topology (or graph) is an effective means to study the reliability of the system. Connectivity, one of the important concepts in graph theory, can be used as an indicator of multiprocessor system’s reliability. However, as classical connectivity is not well suited for practical applications, the concept of$g$-extra connectivity has been proposed to more accurately characterize system’s reliability. The exchanged crossed cube, an innovative network topology derived from the classical hypercube, achieves not only a smaller diameter (improving information transmission efficiency) but also fewer connecting edges (reducing hardware costs when scaling the network). In this article, we derive the$g$-extra connectivity of the exchanged crossed cube and comparatively analyze its advantages in reliability evaluation through simulations.

Urban customized bus (UCB) has become an important mode of urban transportation in recent years. To further enhance the applicability of UCB, we develop a UCB model that accommodates flexible pick-ups and drop-offs in this article. In our model, when a bus passes through a station, it can drop off passengers whose destination is exactly this station and pick up new passengers as long as constraints are satisfied. By eliminating fixed boarding and alighting regions that were commonly adopted in prior studies, we can provide travel services for both prebooked and real-time orders across broader temporal and spatial ranges. Under such a scenario, it is more challenging to decide when and where to pick up which passenger with which bus, and which route the bus chooses. To solve these intractable problems, we proposed an approach including passenger assignment and route design. For passenger assignment, we devise an ant colony system (ACS) method and a greedy method to assign prebooked and real-time passengers to buses, respectively. ACS represents the correlation among different passengers numerically through a pheromone matrix and updates it iteratively based on the good results. Therefore, prebooked passengers with higher correlations are more likely to be assigned to the same buses. The greedy method always selects passengers with the highest heuristic value to have a fast response in real-time passenger assignment. For route design, we adapt a Dijkstra-based greedy method to efficiently design routes for each bus in both static and dynamic situations. At last, experiments based on both generated data and real data demonstrate the superiority of the proposed method.

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IM problem poses further challenges ($\rm i.e.,$high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed-infected1-infected2-infected12-vigilant (SE3IV) model to simulate the information spreading process in multilayer networks. The spreading dynamic is modeled by mean-field equations considering the effect of cross-layer propagation. To optimize the multilayer information maximization modeled by SE3IV, an asynchronous distributed cooperative coevolutionary algorithm (ADCA) is proposed. To improve the efficiency of the algorithm in multilayer networks, the multilayer community detection first decompresses the network into a single layer by dimension-based method. Then, the Louvain method is adopted to decompose the problems into subcomponents with lower dimensionality. The populations with the same size evolve corresponding subcomponents in an asynchronous and distributed way based on the pool model. Besides, an asynchronous communication mechanism is devised to manage the communication among the shared pool. An adaptive seeds regulation strategy is designed to adjust the number of seeds of subcomponents. Numerous experiments on different networks show that ADCA possesses good scalability and efficiency, especially in large-scale networks.

Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the decentralization of local objective functions, it is challenging to optimize the global objective function with efficient communication and guaranteed system consensus. To tackle this challenge, we propose a Multi-Agent Swarm Optimization method with adaptive Internal and External learning (MASOIE). In MASOIE, each agent evolves a swarm of particles by internal learning and external learning. Internal learning enables agents to optimize their local objectives, while external learning enables agents to cooperate to achieve a consensus toward the global objective. To improve the consensus ability, we design a special velocity setting of external learning for particle evolution. We provide the theoretical analysis of the system consensus of deterministic MASOIE. To improve communication efficiency, we design an adaptive communication mechanism to adjust the communication interval, enabling agents to explore at the early stage and reach system consensus at the later stage. Empirical studies show that the proposed algorithm achieves stable consensus performance, competitive solution quality and lower communication cost on benchmark functions compared with existing black-box distributed algorithms.

The widespread adoption of social networks facilitates the dissemination of authentic information while also accelerating the spread of misinformation, such as rumors. The propagation of positive information can enhance user awareness and mitigate the hazards of misinformation. The misinformation containment (MC) problem aims to identify a set o$k$nodes that initiate the spread of positive information, maximizing its influence while minimizing the hazards of misinformation. The greedy approach, which employs extensive Monte Carlo simulations to estimate influence, is time-consuming and can only prioritize either propagation or containment, but not both. This paper studies the MC problem under competitive activation models. Based on geometric models of probability, we calculate the approximate probabilities of nodes being activated by positive information and misinformation at various times. Taking into account the two-hop theory, we propose a consistent and efficient computational method to assess node influence distribution from the perspectives of propagation and containment. This method strikes a balance between propagation and containment, surpassing degree centrality, further informing a heuristic solution to the MC problem. The heuristic solution's overall performance surpasses that of greedy approaches, which can only prioritize one aspect. Experiments on real-world networks demonstrate that our approach effectively balances the propagation of positive information and misinformation containment with low time complexity.

Traffic signal control (TSC) problems have received increasing attention with the development of the smart city. Reinforcement learning (RL) models TSC as a Markov decision process and learns the timing relationship of traffic scheduling from massive historical data. Due to the uncertainty and mutability of TSC problems, existing RL methods face bottlenecks in diversity and are easy to be trapped into local optima. To alleviate this predicament, this paper combines evolutionary optimization and RL to propose an evolutionary algorithm-assisted reinforcement learning (EARL-Light) method for TSC problems. EARL-Light is a population-based algorithm, in which one individual represents a policy and a population of individuals are evolved to search for near-optimal policies. The diversified search ability of evolutionary optimization can help the algorithm get rid of local optima for global optimization and the rapid learning based on the gradient of RL can achieve fast convergence. Extensive experiments on seven real-world traffic datasets demonstrates that EARL-Light achieves shorter travel time with fast convergence.

Target tracking has broad applications like disaster relief, and unmanned aerial vehicles (UAVs) have been universally applied in target tracking in recent years. Due to the strong responsiveness to deceptive reward signals and diverse exploration, evolutionary reinforcement learning (ERL) is a more noteworthy option for training UAVs than common reinforcement learning. However, for ERL contains too many neural networks, its training efficiency is not satisfactory enough. To address this shortcoming, this paper proposes an evolutionary reinforcement learning with double replay buffers (ERLDRB) for UAV online target tracking problem. Firstly, considering the energy consumption and the possible delay of feedback signals to the UAV, a more realistic model of UAV online target tracking problem is designed. Then based on the problem formulation, ERLDRB utilizes a double experience replay buffers technique to increase learning efficiency in the training stage, which can better solve real-world UAV online target tracking problem. Simulation results show that ERLDRB outperforms multiple contrasting algorithms on the designed model.

Traffic assignment problem (TAP) is of great significance for promoting the development of smart city and society. It usually focuses on the deterministic or predictable traffic demand and the vehicle traffic assignment. However, in the real world, traffic demand is usually unpredictable, especially the foot traffic assignment inside buildings such as shopping malls and subway stations. In this work, we consider the dynamic version of TAP, where uncertain commuters keep entering the traffic network constantly. These dynamically arriving commuters bring new challenges to this problem where planning paths for each commuter in advance is incompetent. To address this problem, we propose a genetic programming (GP) hyper-heuristic method to assign uncertain commuters in real-time. Specifically, a low-level heuristic rule called reactive assignment strategy (RAS) is proposed and is evolved by the proposed method. All commuters obey the same strategy to route themselves based on their local observations in a traffic network. Through training based on a designed heuristic template, all commuters will have the ability to find their appropriate paths in real-time to maximize the throughput of the traffic network. This decentralized control mechanism can address dynamically arriving commuters more efficiently than centralized control mechanisms. The experimental results show that our method significantly outperforms the state-of-the-art methods and the evolved RAS has a certain generalization ability.

Many real-world combinatorial optimization problems are defined on networks, such as road networks and social networks, etc. Due to the connectivity nature of networks, decision variables in such problems are usually coupled with each other, and the variables are also closely related to the characteristics of the local subnetwork to which they belong. These features pose new challenges to the design of cooperative coevolutionary (CC) algorithms for the large-scale network-based optimization. To improve the scalability, efficiency, and effectiveness of CC, we propose a new approach called parallel cooperative coevolution with overlapping decomposition and local evaluation (CCOL) for large-scale network-based combinatorial optimization. First, CCOL devises the overlapping decomposition to divide a large-scale network-based problem into some overlapping subproblems with lower dimensions. Second, subproblems are optimized in parallel since they can be evaluated by defined local objectives without the context of other subproblems. Meanwhile, an overlapping cooperation strategy is employed to achieve consensus toward the global objective. Finally, as different subproblems may have different scales, a Huffman-tree-based resources assignment strategy is devised. This strategy is able to utilize computing resources in a better way and thus further improve the scalability of the algorithm. To better demonstrate the proposed CCOL, we implement a set-based particle swarm optimization CCOL (CCOL-SPSO) to solve the multidepot vehicle routing problem with time windows as an example. Experimental results in medium and large scale benchmark problems indicate that CCOL is efficient and promising.

Traffic signals play an important role in traffic management, and traffic dynamics on the road can be adjusted by changing signal timing. Signal timing optimization and traffic flow prediction are traditionally separate. To improve the effect of signal control, a traffic signal control algorithm for urban intersections based on traffic flow prediction is proposed by combining these two technologies. The goal is to minimize the average delay time of the total vehicles at all signalized intersections in the road network. First, a new Prediction-based Signal Control (PSC) model is proposed, which includes a traffic flow prediction module and a signal timing optimization module. Secondly, a traffic flow prediction strategy and a quantum particle swarm optimization algorithm based on phase angle coding is designed to form the signal control algorithm proposed in this paper. Finally, the PSC algorithm is verified with real traffic data. The results show that the proposed algorithm is better than the fixed signal control and traditional adaptive control algorithms, and the reduction of total queue length and average delay time is significantly improved.

In the case of insufficient immune effects such as drugs and vaccines, blockade and isolation of non-drug interventions are effective measures to block the spread of epidemics but their cost cannot be ignored. An economic cost function for non-drug interventions and a multi-objective model for minimizing disease decaying rates and the economic cost are designed. The proposed algorithm combines the priority planning and hierarchical learning swarm optimization (PHSO) with the fast non-dominated sorting method, termed NSPHSO, and it uses an external storage set and selection strategies. The performance of NSPHSO is analyzed and the results show that the collaboration of different levels of isolation measures and resource intervention significantly impacts the spread of epidemics. Taking appropriate isolation measures and resource allocation schemes can ensure that the spread of epidemics is quickly interrupted and the economic cost caused by isolation measures is reduced.

Influence maximization (IM) aims to select a small number of seed users who can maximize the influence of information spread in social networks. The influence maximization problem in multiplex social networks considers the effects of overlapping users between different social networks on spreading the influence across networks. Since nodes in the network have different selection cost, the importance of a node cannot be determined only by the node's influence. This paper proposes a genetic algorithm using a novel node grouping strategy based on the node influence and selection cost, termed NGGA, for multiplex social networks. A node selection operation uses a shielding node set to realize a flexible search. Experimental results on three real multiplex networks demonstrate the effectiveness of the proposed algorithm.

Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to solve data-driven optimization problems. Most existing surrogate-assisted EAs are for centralized optimization and do not take into account the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. To this end, we propose edge-cloud co-EAs (ECCoEAs) to solve distributed data-driven optimization problems, where data are collected by edge servers. Specifically, we first propose a distributed framework of ECCoEAs, which consists of a communication mechanism, edge model management, and cloud model management. This communication mechanism is to avoid deadlock during the collaboration of edge servers and the cloud server. In edge model management, the edge models are trained based on local historical data and data composed of new solutions generated by co-evolutionary and their real evaluation values. In cloud model management, the black-box prediction functions received from edge models are used to find promising solutions to guide the edge model management. Moreover, two ECCoEAs are implemented, which proves the generality of the framework. To verify the performance of algorithms for distributed data-driven optimization problems, we design a novel benchmark test suite. The performance on the benchmarks and practical distributed clustering problems shows the effectiveness of ECCoEAs.

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences to guide crowd movement has become a widely adopted approach to alleviate congestion. This work presents a method that combines crowd simulation and management, focusing on the optimization of the fence layout for efficient crowd guidance. First, a congestion probability social force model (CP-SFM) is introduced to simulate the irrational pedestrians and to evaluate the efficacy of different fence layouts. Second, based on CP-SFM, we are the first to formulate the fence layout problem as an optimization problem with the objective to minimize the congestion of pedestrians in public places. Third, we further propose an ant colony crowd intervention algorithm (ACCI) to optimize the layout of fences. Lastly, we illustrate the performance of proposed ACCI on 18 scenarios including two real-world subway stations. Compared with other optimization methods, ACCI demonstrates promising performance in avoiding crowd congestion.

Constraint handling is a core part when using surrogate-assisted evolutionary algorithms (SAEAs) to solve expensive constrained optimization problems (ECOPs). However, most existing SAEAs for ECOPs train a surrogate for each constraint. With the number of constraints increasing, the training burden of surrogates becomes heavy and the efficiency of the algorithm is greatly reduced. To solve this issue, this article proposes an efficient two-stage surrogate-assisted differential evolution (eToSA-DE) algorithm to handle expensive inequality constraints. eToSA-DE trains one surrogate for the degree of constraint violation and the type of the surrogate varies during the evolution process. In the first stage when there are only a few feasible individuals, a Gaussian process regression model is trained to fit the degree of constraint violation. In the second stage when more feasible individuals are accumulated, a support vector machine classification model is trained to classify whether candidates are feasible. Both types of surrogates are constructed by individuals which are chosen by the boundary training data selection strategy. These selected individuals are located around the feasible boundaries and helpful for the surrogate to approximate the feasibility structure. Besides, a feasible exploration strategy is devised to search for promising areas. To alleviate the error caused by the regression model, a nearest neighbor rectification is adopted to modify the prediction results. Extensive experiments on benchmark test functions and two formulated engineering optimization problems demonstrate that the proposed method can get satisfactory optimization results and significantly improve the efficiency of the algorithm.

Multi-target surveillance is an important application of path planning in the field of autonomous vehicles (AV). The existing researches are limited to either static or random obstacle environments. A model-based moving obstacle environment is proposed in this paper. By setting the start point of the AV, when evaluating the path, the moving process of AV is monitored. The positions of obstacles are updated according to the time, as well as the path threat. The cost of the route between any two targets depends not only on the distance, but also the path threat value weighted by a penalty factor. In this paper, a set-based comprehensive learning particle swarm optimization with local search (S-CLPSO+LS) is proposed to find the optimal path. The influence of the penalty factor on the obstacle avoidance ability of the algorithm is discussed. Four dynamic scenarios with different characteristics are designed and analyzed, and other path planning algorithms have been used for comparative experiments.

In order to make full use of the surplus power in a coalition structure, this paper proposes a new coalition structure generation strategy based on population-based incremental learning (PBIL) and particle swarm optimization (PSO). In order to improve the accuracy of the algorithm and the overall benefits of the coalition structure, the proposed algorithm integrates a modified position update formula of PSO and the state transition rules in PBIL based on the probability vector to optimize a population. Compared with the existing seven intelligent optimization algorithms, empirical results show that the proposed algorithm is more conducive to optimize coalition structures than other algorithms.

Feature selection (FS) is to select relevant feature subsets from a given feature set to reduce the dimensions of data. In the classification task, feature selection can not only reduce the calculation cost but also improve the classification accuracy. In this paper, a multimodal bare-bone niching differential evolution algorithm (MBNDE) is proposed for FS in classification problems. MBNDE finds multiple feature combinations that can achieve the highest classification rate. Three different strategies have been applied to MBNDE in the niching process, and the corresponding algorithms are termed MBNDE-CC (crowding and cluster), MBNDE-CS (crowding and speciation), as well as MBNDE (index-based). A modified 3-NN classifier is designed to evaluate the classification rate of different feature combinations. The results of the proposed method and some existing multimodal and unimodal algorithms for FS are compared. The proposed algorithm shows promising performance.

The surrogate-assisted optimization algorithm is a method to solve expensive optimization problems by constructing a predicted evaluation model to replace the real objective function. The evaluation of the objective function is generally time-consuming and the target is to use a small amount of exact function evaluation (EFE) to achieve better solutions in shorter time. The generation and selection of the candidate solutions to have the EFE are the most important. For the generation of candidate solutions, this paper proposes an ensemble method for two state-of-the-art models, i.e. the Gaussian process (GP) and the Radial basis function (RBF) model to have better prediction of the solutions. For the selection of candidate solutions, the traditional similarity-based multipoint infill criterion (SMIC) strategy is modified and the proposed method is termed the best SMIC (bSMIC). The social learning particle swarm optimization (SLPSO) algorithm is used as the basic optimization algorithm. The effectiveness of the proposed ensemble surrogate-assisted SLPSO (ESLPSO) has been fully analyzed in various problems and compared with other algorithms.

Multi-solution traveling salesman problem has diverse optimal routes. To obtain the different optimal solutions, current researches incorporate evolutionary algorithms with niching techniques. However, without knowing the problem characteristics in advance, the algorithms suffer from difficulties in setting the niching parameters. To address this issue, we utilize a graph neural network to predict a prior knowledge about the optimal tour length. Then, with the prior estimate, the niche radius can be adjusted for a specific problem. We develop a niching evolutionary algorithm that utilizes the calculated niche radius to identify diverse niches. Besides, a selective local search strategy is embedded into the algorithm to enhance the search capability. The experimental results show that the proposed algorithm has a competitive performance over the comparison algorithms on the benchmark suite.

Virtual network embedding (VNE) is the key technology in network virtualization and has been proven NPhard. The purpose of VNE is to find the optimal mapping of virtual nodes and links, and minimize the utilization of resources. However, many particle swarm optimization approaches to VNE separate VNE into two independent subproblems (i.e., node mapping and link mapping) and ignore the coordination between node mapping and link mapping. In this paper, a one-stage and dual-heuristic particle swarm optimization (DH-PSO) is devised to solve VNE. To coordinate node mapping and link mapping, firstly, DH-PSO updates positions of particles step by step, and nodes and links are mapped in one stage. Secondly, DH-PSO devises the dual-heuristic strategy to further improve the optimizing capability. The first heuristic strategy is to construct a candidate set and the second strategy is to find the best solution from the candidate set. Hence, not only the network resources but the network paths are taken into account to construct solutions. DH-PSO can be combined with different two-stage approaches to become one-stage. DH-PSO is experimentally studied on different instances. The experimental results verify that the proposed DH-PSO is promising.

The virtual machine (VM) placement problem with the objective to save energy consumption and improve machine utility has been studied extensively in Cloud computing. However, the connection information among VMs during the execution of scientific workflows is seldom considered in existing studies. Therefore, this paper intends to build a novel connection-aware model for VM placement in scientific workflows. Different from existing studies, as the connection information of VMs is considered following the topology of workflows, not only the CPU capacity and memory capacity but also the transmission bandwidth among machines should be considered. An energy- aware, traffic-aware, connection-aware ant colony optimization (ETCACO) approach is developed. The proposed ETCACO combines Ant Colony Optimization (ACO) with a scheduler, namely greedy placeman. Experiments are performed to compare the proposed model with the traditional approach. It is discovered that by taking the connection information into consideration, the proposed approach can reduce energy consumption by 7%.

The aircraft arrival scheduling (AAS) problem is an important issue in airport management that requires well-designed scheduling approaches to help improve the operating efficiency of airports. However, most of the existing approaches are based on a single runway while the multi-runway situation is more common and more complex in reality. In order to solve the multi-runway AAS (MRAAS) problem, this paper proposes a receding horizon control (RHC) based two-level ant colony system (ACS) approach. The RHC divides the problem with receding time windows. Therefore, the number of aircraft considered in each RHC stage is reduced, so that the ACS algorithm can endure less computational burden and search for good scheduling sufficiently. Moreover, in each RHC stage to schedule the aircraft, a two-level scheduling strategy is proposed to assist the ACS to deal with the multi-runway difficulty. During the first level scheduling, the ACS algorithm schedules aircraft based on a single runway. Then in the second level scheduling, we assign the aircraft to real multi-runways based on both the obtained sequence of the first level scheduling and the real occupancy condition of each runway. We have conducted experiments based on case study and compared the results with first come first serve (FCFS) approach, showing the feasibility as well as the higher performance of the proposed RHC-ACS-MRAAS approach.

Cartesian Genetic Programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.

In cloud computing, scheduling plays a critical role for quality of service (QoS) and provider efficiency which are generally measured by several metrics and make the scheduling a multiobjective problem (MOP). In this paper, we propose a differential evolution algorithm with double-level archives (DE-DLA) for bi-objective cloud task scheduling. The proposed algorithm is based on the newly-developed framework, multiobjective evolutionary algorithm with double-level archives (MOEA-DLA), and uses differential evolution to implement this framework. Global Archive is used to save Pareto-optimal individuals for the whole problem and Sub-archive is used to save several comparatively good individuals for the corresponding sub-problem formed by decomposition. So the algorithm takes advantages of both whole multiobjective problem optimization and decomposition based optimization. Precedence constraint in user's application is considered in the scheduling model of this paper. To minimize cost and makespan simultaneously, the proposed algorithm tries to find optimal resource allocation and optimal order of task executing. In the experiment, compared with two other algorithms, DE-DLA has shown competitive advantages.

This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.

In this work we focus on reconstruction of cross-cut shredded text documents (RCCSTD), which is of high interest in the fields of forensics and archeology. A novel genetic algorithm, with splicing-driven crossover, four mutation operators, and a row-oriented elitism strategy, is proposed to improve the capability of solving RCCSTD in complex space. We also design a novel and comprehensive objective function based on both edge and empty vector-based splicing error to guarantee that the correct reconstruction always has the lowest cost value. Experiments are conducted on six RCCSTD scenarios, with experimental results showing that the proposed algorithm significantly outperforms the previous best-known algorithms for this problem.

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

This paper takes the multiobjective traveling salesman problem (MOTSP) as the representative for multiobjective combinatorial problems and develop a set-based comprehensive learning particle swarm optimization (S-CLPSO) with decomposition for solving MOTSP. The main idea is to take advantages of both the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework and our previously proposed S-CLPSO method for discrete optimization. Consistent to MOEA/D, a multiobjective problem is decomposed into a set of subproblems, each of which is represented as a weight vector and solved by a particle. Thus the objective vector of a solution or the cost vector between two cities will be transformed into real fitness to be used in S-CLPSO for the exemplar construction, the heuristic information generation and the update of pBest. To validate the proposed method, experiments based on TSPLIB benchmark are conducted and the results indicate that the proposed algorithm can improve the solution quality to some degree.

In the application of wireless sensor networks (WSNs), energy efficiency is always an important topic for extending the network lifetime. Scheduling the operation modes of sensors can reduce the energy wasted by redundant sensors, but frequent alterations of sensors' modes and different data transmission routes influence the reliability of the WSN. In this paper, a novel algorithm for maximizing the reliable working lifetime of a WSN is proposed. The considered problem restricts the minimum working period of the sensors before changing their operation modes. In order to extend the working lifetime of a reliable WSN, the proposed algorithm adopts the ant colony optimization (ACO) for finding the maximum number of reliable working periods of sensors. The performance of the proposed algorithm has been tested on various WSNs and compared with the state-of-the-art algorithms. The results show that the proposed algorithm can enhance the scheduling reliability of WSNs.

Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes (in RCGAs each chromosome is a floating point vector). It operates on a whole chromosome instead of several single genes to produce the new chromosome. The linear map is represented by a randomly generated mapping matrix which satisfies some predefined constraints. By this way, the constraints restrict the mutations of genes on a same chromosome as a whole. RCGA with the proposed mutation scheme is tested on 16 benchmark functions. Results demonstrate that the proposed scheme not only improves the solution accuracy that RCGA can obtain, but also presents a very fast convergence speed. The linear map-based mutation scheme has a bright future to improve RCGAs.

Research into maximizing the network lifetime is one of the most significant and challenging areas in wireless sensor networks (WSNs). By arranging sensors and sinks to realize target coverage and network connectivity respectively, an efficient schedule of sensors and sinks can prolong the network lifetime. However, the arrangements of sensors and sinks correlate with each other because each sensor needs to send its data to a sink, making the problem of finding the optimal schedule difficult. Instead of using a single process to optimize the entire schedule of sensors and sinks, this paper proposes a scheduling method which uses two separate processes to schedule operations of sensors and sinks respectively. The first process organizes sensors in the network into disjoint sets, with each set being able to fully cover the targets. Based on the arrangement of sensors, a novel genetic algorithm (GA) is adopted in the second process to allocate sinks to each set of sensors. When the number of full cover sets that ensure both connectivity of sensors to sinks and connectivity of the network composed of sinks is maximized, a schedule that maximizes the network lifetime can be obtained. The proposed method has been applied to a number of WSN cases. Results demonstrate that the method is effective and efficient in prolonging the lifetime of WSNs.

Scheduling activities of network devices is an important and promising methodology for prolonging the lifetime of wireless sensor networks (WSNs). The existing scheduling methods in the literature are mostly designed for homogeneous WSNs. Heterogeneous WSNs, despite their wide applications, have received few research attentions. This paper proposes an ant-colony-system-based scheduling method (ACS-SM) for maximizing the lifetime of a typical type of heterogeneous WSNs. First, the lifetime maximization problem is formulated as finding the maximum number of disjoint sets of devices, with each set fulfilling sensing coverage and network connectivity simultaneously. Then ACS-SM adapts the incremental solution construction mechanism in ACS for building disjoint connected cover sets on the basis of a well-designed construction graph. Pheromone trails that record search experience and heuristic information that combines domain knowledge are utilized to guide the set building procedure. A local search process is also developed to further enhance the efficiency of the method. ACS-SM is applied to fifteen WSN cases in three series. Experimental results show that the proposed method can find high-quality solutions at a fast speed for WSNs with different characteristics.

Since sensors are energy-limited in a wireless sensor network (WSN), how to balance the energy consumption among the sensors in the network so as to prolong the lifetime of the whole network is a critical research topic in the development of WSNs. In this paper, a novel ant routing optimization (ARO) algorithm is proposed for taking advantage of the redundant sensors in the network to help relay the sensed data traffic to the sink. By choosing suitable relay nodes, the lifetime of the sensing sensors are extended, resulting in the extension of the whole network lifetime. The ARO algorithm searches energy-efficient routing trees by considering the energy consumption of sensing, data transmission and reception. Simulation results show that ARO finds much better routing trees than the minimum transmission energy routing scheme.

Maximizing the lifetime of a sensor network by scheduling operations of sensors is an effective way to construct energy efficient wireless sensor networks. After the random deployment of sensors in the target area, the problem of finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target area, is nondeterministic polynomial-complete. This paper proposes a hybrid approach of combining a genetic algorithm with schedule transition operations, termed STHGA, to address this problem. Different from other methods in the literature, STHGA adopts a forward encoding scheme for chromosomes in the population and uses some effective genetic and sensor schedule transition operations. The novelty of the forward encoding scheme is that the maximum gene value of each chromosome is increased consistently with the solution quality, which relates to the number of disjoint complete cover sets. By exerting the restriction on chromosomes, the forward encoding scheme reflects the structural features of feasible schedules of sensors and provides guidance for further advancement. Complying with the encoding requirements, genetic operations and schedule transition operations in STHGA cooperate to change the incomplete cover set into a complete one, while the other sets still maintain complete coverage through the schedule of redundant sensors in the sets. Applications for sensing a number of target points, termed point-coverage, and for the whole area, termed area-coverage, have been used for evaluating the effectiveness of STHGA. Besides the number of sensors and sensors' sensing ranges, the influence of sensors' redundancy on the performance of STHGA has also been analyzed. Results show that the proposed algorithm is promising and outperforms the other existing approaches by both optimization speed and solution quality.

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person's skill. This paper develops a novel intelligent testing system for both teachers and students. Equipped with user-friendly interfaces and administrative modules, the proposed system offers the following features and advantages: 1) Self-adaptive. Item attributes in an item bank are adaptively updated to reflect students' newest learning states. 2) Reliable. Tests with high assessment qualities are reliably generated, satisfying teachers' multiple requirements. 3) Flexible for generating parallel tests with identical test ability, especially useful for makeup exams. For students, the system is used for exercises and self-evaluation. For teachers, the system is a good helper for generating tests with different requirements. In this paper, the self-adaptation strategy and the ant colony optimization based test composition (ACO-TC) method are firstly described. ACO, an advanced computational intelligence algorithm, is used for searching high-quality results. Then the proposed testing system is introduced. The performance of the system is analyzed for composing tests in different situations.

In the ant colony system (ACS) algorithm, ants build tours mainly depending on the pheromone information on edges. The parameter settings of pheromone updating in ACS have direct effect on the performance of the algorithm. However, it is a difficult task to choose the proper pheromone decay parameters ¿ and ¿ for ACS. This paper presents a novel version of ACS algorithm for obtaining self-adaptive parameters control in pheromone updating rules. The proposed adaptive ACS (AACS) algorithm employsAverage Tour Similarity(ATS) as an indicator of the optimization state in the ACS. Instead of using fixed values of ¿ and ¿, the values of ¿ and ¿ are adaptively adjusted according to the normalized value of ATS. The AACS algorithm has been applied to optimize several benchmark TSP instances. The solution quality and the convergence rate are favorably compared with the ACS using fixed values of ¿ and ¿. Experimental results confirm that our proposed method is effective and outperforms the conventional ACS.

The SET k-cover problem is an NP-complete combinatorial optimization problem, which is derived from constructing energy efficient wireless sensor networks (WSNs). The goal of the problem is to find a way to divide sensors into disjoint cover sets, with every cover set being able to fully cover an area and the number of cover sets maximized. Instead of using deterministic algorithms or simple genetic algorithms (GAs), this paper presents a hybrid approach of a GA and a stochastic search. This approach comprises two core modules. The first is the interaction module, which is applied to improve the quality of the population through interaction of individuals. The second is the self construction module, which is a stochastic search procedure running without interaction of individuals. The interaction module is implemented as a combination of selection and crossover, which can efficiently exploit the solutions currently found. The self-construction module includes an adjusted mutation operation and three additional operations. This module is the main force to explore the solution space which can eliminate the inefficiency of using classical GA operations to explore the solution space. Experimental results show that the propose algorithm performs better than the other existing approaches.

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person's skill. This paper develops a novel intelligent testing system for both teachers and students. Based on the browser/server structure, the proposed testing system comprises a question bank and five modules, offering the features of self-adaptation, reliability, and flexibility for generating parallel tests with identical test ability. The core of the developed system is the ant-colony-optimization-based test composition (ACO-TC) method, which aims at generating high-quality tests for examinations and satisfying multiple requirements. As an advanced computational intelligence algorithm, the proposed ACO-TC method uses a colony of ants to select appropriate questions from a question bank to construct solutions. Pheromone and heuristic information is designed for facilitating the ants' selection. The system is analyzed by composing tests in different situations. The generated tests not only match the expected total completion time, the concept proportions, the average difficulty, and the score proportions of different question types, but also have high average discrimination degrees of questions. The experimental results also show that the system can always generate high-quality tests from question banks with various sizes.

This paper presents an enhanced Latin square genetic algorithm (LSGA). It makes the chromosomes to be more sensible to their surrounding regions. The algorithm applies orthogonal design method to every chromosome in the population to detect chromosomes with high fitness values in the surrounding regions. Orthogonal design method makes it more concise and direct to find the delegate to represent the situation of the surrounding regions. We execute the proposed algorithm to solve 15 test functions and compare it with traditional algorithm without using orthogonal design method. The results show that the proposed algorithm can find optimal or close-to-optimal solutions with higher speed and more accuracy.