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WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator’s mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.

This paper presents a novel differential evolution (DE) framework with a self-regulated neighborhood, termed self-regulated differential evolution (SrDE), for real parameter optimization. The novelty and advantage of SrDE are to present a self-regulated neighborhood (SrN) for learning, regulating, and using the neighborhood information of the population in guiding the search process of DE. Specifically, SrDE is characterized by the following three aspects. First, a one-dimensional self-organizing map (SOM) method is employed to dynamically construct the neighborhood relationships between individuals. Second, a self-sizing technique is applied to adaptively regulate the neighborhood size of each individual based on its search state. Third, a neighborhood path-assisted strategy is proposed to utilize promising neighborhood information for guiding the mutation process. Extensive experiments on a suite of real-parameter functions and real-world problems have demonstrated the superior and competitive performance of SrDE when compared with the state-of-the-art DE variants.

This article focuses on a new application-oriented variant of vehicle routing problem. This problem comes from the daily distribution scenarios of a real-world logistics company. It is a large-scale (with customer sizes up to 2000), many-objective (with six objective functions) NP-hard problem with six constraints. Then, a local search with chain search path strategy (LS-CSP) is proposed to effectively solve the problem. It is a decomposition-based algorithm. First, the considered problem is decomposed into multiple single-objective subproblems. Then, local search is applied to solve these subproblems one by one. The advantage of the LS-CSP lies in a chain search path strategy, which is designed for determining the order of solving the subproblems. This strategy can help the algorithm find a high-quality solution set quickly. Finally, to assess the performance of the proposed LS-CSP, three instance sets containing 132 instances are provided, and four state-of-the-art decomposition-based approaches are adopted as the competitors. Experimental results show the effectiveness of the proposed algorithm for the considered problem.

Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

In differential evolution (DE), the salient feature lies in its mutationmechanismthat distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms, the parents for mutation are randomly chosen from the current population. Hence, all vectors of population have the equal chance to be selected as parents without selective pressure at all. In this way, the information of population cannot be fully exploited to guide the search. To alleviate this drawback and improve the performance of DE, we present a new selection method of parents that attempts to choose individuals for mutation by utilizing the population information effectively. The proposed method is referred as fitnessand- position based selection (FPS), which combines the fitness and position information of population simultaneously for selecting parents in mutation of DE. In order to evaluate the effectiveness of FPS, FPS is applied to the original DE algorithms, as well as several DE variants, for numerical optimization. Experimental results on a suite of benchmark functions indicate that FPS is able to enhance the performance of most DE algorithms studied. Compared with other selection methods, FPS is also shown to be more effective to utilize information of population for guiding the search of DE.

Wireless sensor networks (WSNs) have been deployed for many applications of target detection, such as intrusion detection and wildlife protection. In these applications, the first step is to detect whether the target is present or not. However, most of the existing work uses the “simple disk model” as signal model, which may not capture the sensing environment. In this work, we utilize a more realistic signal model to describe sensing process of sensors. On the other hand, the “majority rule” is widely used to make the final decision, which may not obtain the true judgment. To this end, we utilize a more realistic signal model and also use a probabilistic decision model to make the final decision. Moreover, we propose a probabilistic detection algorithm in which all sensors’ local measurement values are fully used. This algorithm does not need any artificial threshold compared with traditional algorithms. It makes the most of spatiotemporal information to obtain the final decision. For the spatial perspective, sensors are distributed in different locations cooperating with each other. Meanwhile, for the temporal perspective, multiround subdecisions are fused. The effectiveness of the proposed method is validated by extensive simulation results, which show high detection probabilities and low false alarm probabilities.

Satellite communications technology leads to an important improvement in our life and world. The frequency assignment problem (FAP) is a fundamental problem in satellite communication system for providing high-quality transmissions. The whole goal of the FAP in satellite communication system is to minimize co-channel interference between two satellite systems by rearranging frequency assignment. Recently, many metaheuristics, including neural networks and evolutionary algorithms, are proposed for this NP-complete problem. All such algorithms formulate the FAP as a single-objective problem, although it obviously has two objectives and thus essentially is a multiobjective optimization problem. This study explicitly formulates FAP as a multiobjective optimization problem and presents a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with a problem-specific subproblem-dependent heuristic assignment (SHA), called MOEA/D-SHA, for the multiobjective FAP. Simulation results show that the MOEA/D-SHA outperforms significantly general-purpose MOEA/D, and an off-the-shelf multiobjective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SHA over the state-of-the-art single-objective approaches are also shown.

The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW.

The multiobjective unconstrained binary quadratic programming problem is an important combinatorial optimization problem with both theory and practical values. Until now, several efforts have been made to design metaheuristic methods to solve the problem. However, designing such effective methods is not trivial and heavily depends on experts' specific knowledge. Meanwhile, due to the iterative nature of metaheuristic methods, they require a long time to find high-quality solutions. From the perspective of machine learning, this paper proposes a deep reinforcement learning method to solve the problem. The method can automatically learn effective heuristics from a large amount of data, thus decreasing the need for experts' knowledge. Meanwhile, by leveraging the power of GPU, the method can quickly obtain high-quality solutions for a batch of instances. Experimental results show the proposed method outperforms two classical metaheuristic methods in terms of solution quality and running time for solving the problem.

Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically. Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS). In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy. They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least comparable to several state-of-art DE variants as well as some other evolutionary algorithms.