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Wanliang Wang
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

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Journal article
Published: 07 July 2021 in Information Sciences
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Multimodal multi-objective optimization problems (MMOPs) involve locating equivalent Pareto optimal solutions in decision space with the same objective values. The key to handling MMOPs is finding all equivalent Pareto optimal solutions and maintaining a promising balance between the convergence and diversity of solutions in both decision space and objective space. To tackle this issue, a self-organizing quantum-inspired particle swarm optimization algorithm (MMO_SO_QPSO) is proposed in this paper for handling MMOPs. In the proposed MMO_SO_QPSO, a self-organizing map is used to find the best neighbor leader of particles. With the aid of neighbor leader particles, a special zone searching method is adopted to update the position of particles and locate equivalent Pareto optimal solutions in decision space. To maintain diversity and convergence of Pareto optimal solutions, a special archive mechanism that relies on the maximum-minimum distance among solutions is introduced into MMO_SO_QPSO. And some outstanding Pareto optimal solutions are maintained in the special archive. In addition, a new performance indicator is developed to estimate properly the similarity between obtained Pareto optimal solutions and true Pareto optimal solutions. The performance of the proposed MMO_SO_QPSO is compared with six state-of-the-art multimodal multi-objective evolutionary algorithms on two well-known benchmark problems. Experimental results demonstrate the superior performance of MMO_SO_QPSO for solving MMOPs. The effectiveness of several strategies is also discussed.

ACS Style

Guoqing Li; Wanliang Wang; Weiwei Zhang; Wenbo You; Fei Wu; Hangyao Tu. Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization. Information Sciences 2021, 577, 510 -540.

AMA Style

Guoqing Li, Wanliang Wang, Weiwei Zhang, Wenbo You, Fei Wu, Hangyao Tu. Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization. Information Sciences. 2021; 577 ():510-540.

Chicago/Turabian Style

Guoqing Li; Wanliang Wang; Weiwei Zhang; Wenbo You; Fei Wu; Hangyao Tu. 2021. "Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization." Information Sciences 577, no. : 510-540.

Journal article
Published: 28 May 2021 in Mathematics
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The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance the diversity and convergence performance and improve the performance in high-dimensional cases for 0-1 knapsack problems. The proposed algorithm adopts quantum computing principles such as quantum superposition states and quantum gates. It also uses adaptive mutation operations of differential evolution, crossover operations of differential evolution, and quantum observation to generate new solutions as trial individuals. Selection operations are used to determine the better solutions between the stored individuals and the trial individuals created by mutation and crossover operations. In the event that the trial individuals are worse than the current individuals, the adaptive grey wolf optimizer and quantum rotation gate are used to preserve the diversity of the population as well as speed up the search for the global optimal solution. The experimental results for 0-1 knapsack problems confirm the advantages of QDGWO with the effectiveness and global search capability for knapsack problems, especially for high-dimensional situations.

ACS Style

Yule Wang; Wanliang Wang. Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem. Mathematics 2021, 9, 1233 .

AMA Style

Yule Wang, Wanliang Wang. Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem. Mathematics. 2021; 9 (11):1233.

Chicago/Turabian Style

Yule Wang; Wanliang Wang. 2021. "Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem." Mathematics 9, no. 11: 1233.

Article
Published: 13 April 2021 in Applied Intelligence
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In the multimodal multi-objective optimization problems (MMOPs), at least two equivalent Pareto optimal solutions in decision space with an identical objective value are desired. The challenge for solving MMOPs is locating equivalent Pareto optimal solutions in decision space, and maintaining a fine balance between diversity and convergence of Pareto optimal solutions in both decision space and objective space, simultaneously. To address this issue, a success-history based parameter adaptation for multimodal multi-objective differential evolution algorithm using fitness sharing (MMOSHADE) is proposed in this paper. A success-history based parameter adaptation for differential evolution (SHADE) is integrated into MMOSHADE to find elite individuals and locate Pareto optimal solutions in decision space. Subsequently, a modified selection operation in differential evolution (DE) is introduced into MMOSHADE to explore outstanding convergence solutions. Furthermore, a double fitness sharing method is available for maintaining the diversity of Pareto optimal solutions in both decision space and objective space, simultaneously. The proposed MMOSHADE is performed on three categories of problems to test the performance of MMOSHADE. The comparison between MMOSHADE and six competing algorithms demonstrates the superiority of the proposed MMOSHADE in solving MMOPs and large-scale polygon-based MMOPs. MMOSHADE is also capable of finding the entire Pareto front in most cases when it is used to address multi-objective optimization problems. Additionally, the effectiveness of several strategies is validated by the designed experiments, and the parameters involved in MMOSHADE are discussed.

ACS Style

Guoqing Li; Wanliang Wang; Haoli Chen; Wenbo You; Yule Wang; Yawen Jin; Weiwei Zhang. A SHADE-based multimodal multi-objective evolutionary algorithm with fitness sharing. Applied Intelligence 2021, 1 -33.

AMA Style

Guoqing Li, Wanliang Wang, Haoli Chen, Wenbo You, Yule Wang, Yawen Jin, Weiwei Zhang. A SHADE-based multimodal multi-objective evolutionary algorithm with fitness sharing. Applied Intelligence. 2021; ():1-33.

Chicago/Turabian Style

Guoqing Li; Wanliang Wang; Haoli Chen; Wenbo You; Yule Wang; Yawen Jin; Weiwei Zhang. 2021. "A SHADE-based multimodal multi-objective evolutionary algorithm with fitness sharing." Applied Intelligence , no. : 1-33.

Journal article
Published: 28 January 2021 in Swarm and Evolutionary Computation
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In the multimodal multi-objective optimization problems (MMOPs), there may exist two or multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of solving MMOPs lies in how to locate more equivalent PS in decision space and maintain a promising balance between the diversity of Pareto optimal solutions in decision space and the convergence of Pareto optimal solutions in objective space at the same time. To address these issues, a grid search based multi-population particle swarm optimization algorithm (GSMPSO-MM) is proposed in this paper to handle MMOPs. Multi-populations based on the k-means clustering method is adopted to locate more equivalent PS in decision space, and a grid is applied to explore high-quality solutions in decision space in GSMPSO-MM. The environmental selection operator, including the removing inefficient solutions operator and the updating non-dominated solutions archive, aims to approach the true non-dominated solutions, where the updating non-dominated solution archive is responsible for developing the diverse solutions in both the decision and objective space, simultaneously. Besides, the purpose of removing inefficient solutions with inferior convergence in objective space is to maintain promising convergence solutions in objective space. GSMPSO-MM is compared with seven state-of-the-art algorithms on a well-known MMOPs benchmark function. Experimental results demonstrate the superior performance of our proposed algorithm in solving MMOPs.

ACS Style

Guoqing Li; Wanliang Wang; Weiwei Zhang; Zheng Wang; Hangyao Tu; Wenbo You. Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization. Swarm and Evolutionary Computation 2021, 62, 100843 .

AMA Style

Guoqing Li, Wanliang Wang, Weiwei Zhang, Zheng Wang, Hangyao Tu, Wenbo You. Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization. Swarm and Evolutionary Computation. 2021; 62 ():100843.

Chicago/Turabian Style

Guoqing Li; Wanliang Wang; Weiwei Zhang; Zheng Wang; Hangyao Tu; Wenbo You. 2021. "Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization." Swarm and Evolutionary Computation 62, no. : 100843.

Journal article
Published: 11 November 2020 in BMC Bioinformatics
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Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

ACS Style

Zhaojuan Zhang; Wanliang Wang; Ruofan Xia; Gaofeng Pan; Jiandong Wang; Jijun Tang. Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm. BMC Bioinformatics 2020, 21, 1 -30.

AMA Style

Zhaojuan Zhang, Wanliang Wang, Ruofan Xia, Gaofeng Pan, Jiandong Wang, Jijun Tang. Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm. BMC Bioinformatics. 2020; 21 (1):1-30.

Chicago/Turabian Style

Zhaojuan Zhang; Wanliang Wang; Ruofan Xia; Gaofeng Pan; Jiandong Wang; Jijun Tang. 2020. "Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm." BMC Bioinformatics 21, no. 1: 1-30.

Journal article
Published: 23 October 2020 in Mathematics
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In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.

ACS Style

Zhaojuan Zhang; Wanliang Wang; Gaofeng Pan. A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem. Mathematics 2020, 8, 1860 .

AMA Style

Zhaojuan Zhang, Wanliang Wang, Gaofeng Pan. A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem. Mathematics. 2020; 8 (11):1860.

Chicago/Turabian Style

Zhaojuan Zhang; Wanliang Wang; Gaofeng Pan. 2020. "A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem." Mathematics 8, no. 11: 1860.

Research article
Published: 07 August 2020 in Computational Intelligence and Neuroscience
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In image denoising (IDN) processing, the low-rank property is usually considered as an important image prior. As a convex relaxation approximation of low rank, nuclear norm-based algorithms and their variants have attracted a significant attention. These algorithms can be collectively called image domain-based methods whose common drawback is the requirement of great number of iterations for some acceptable solution. Meanwhile, the sparsity of images in a certain transform domain has also been exploited in image denoising problems. Sparsity transform learning algorithms can achieve extremely fast computations as well as desirable performance. By taking both advantages of image domain and transform domain in a general framework, we propose a sparsifying transform learning and weighted singular values minimization method (STLWSM) for IDN problems. The proposed method can make full use of the preponderance of both domains. For solving the nonconvex cost function, we also present an efficient alternative solution for acceleration. Experimental results show that the proposed STLWSM achieves improvement both visually and quantitatively with a large margin over state-of-the-art approaches based on an alternatively single domain. It also needs much less iteration than all the image domain algorithms.

ACS Style

Yanwei Zhao; Ping Yang; Qiu Guan; Jianwei Zheng; Wanliang Wang. Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization. Computational Intelligence and Neuroscience 2020, 2020, 1 -12.

AMA Style

Yanwei Zhao, Ping Yang, Qiu Guan, Jianwei Zheng, Wanliang Wang. Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization. Computational Intelligence and Neuroscience. 2020; 2020 ():1-12.

Chicago/Turabian Style

Yanwei Zhao; Ping Yang; Qiu Guan; Jianwei Zheng; Wanliang Wang. 2020. "Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization." Computational Intelligence and Neuroscience 2020, no. : 1-12.

Journal article
Published: 17 July 2020 in Journal of Cleaner Production
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Sustainable development is critical to cold chain logistics, including its economic, environmental, and social effects, especially in road transportation. To simultaneously address these issues, we propose a comprehensive cold-chain-based low-carbon location-routing-problem optimization model to minimize the total logistics costs and client and vehicle waiting time. The first objective comprises the fixed costs of depots to open and vehicles to rent, vehicle renting cost, driver salaries, fuel consumption cost, carbon emission costs, and damage costs of cargos that need to be refrigerated or frozen. The second objective consists of the waiting time of clients and vehicles to improve client satisfaction and the efficiency of the cold chain logistics network. In the proposed problem, we developed a strategy for improving the efficiency of the cold chain logistics network by mixing the types of cargos arranged in one vehicle. Aiming at efficiently solving the proposed model, six well-known multi-objective evolutionary algorithms (MOEAs) were used by combining an efficient framework, and first (FI) and best-improvement (BI) search mechanisms were considered. In the experiments, we examined the effectiveness of six MOEAs inserting the proposed framework and search mechanisms, and the result showed that NSGA-II/FI, SPEA2/FI, and NSGA-II/BI were the top three MOEAs. In the extensive experiments, the results showed that the delivery strategy, depot cost, depot capacity, crowding distance, and traveling speed have significant effects on the Pareto front, fuel consumption, carbon emission, vehicle and client waiting times, traveling distance, and traveling time.

ACS Style

Longlong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production 2020, 273, 122801 .

AMA Style

Longlong Leng, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Jingling Zhang, Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production. 2020; 273 ():122801.

Chicago/Turabian Style

Longlong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. 2020. "Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches." Journal of Cleaner Production 273, no. : 122801.

Article
Published: 11 June 2020 in Applied Intelligence
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Deep learning frameworks(such as deep convolutional networks) require data to have a regular shape. However, discrete features extracted from heterogeneous data cannot be collected in a regular shape to convolute. In this article, a Two-Dimensional Discrete Feature Based Spatial Attention CapsNet(TDACAPS) is proposed to convert one-dimensional discrete features into two-dimensional structured data through Cartesian Product for surface electromyogram(sEMG) signal recognition. sEMG signal varies from person to person is the main signal source of prosthetic control. Our model transforms multi-angle discrete features into structured data to find the inherent law of sEMG signal. Due to uneven information distribution of structured data, this model combines capsule network with attention mechanism to place emphasis on abundant information regions and reduce ancillary information loss. Extensive experiments show our model yields an improvement for sEMG signal recognition of almost 3% than capsule network and other neural networks under different conditions. Our attention mechanism that employs overlapping pooling to search feature map weight is preferable to the squeeze-and-excitation module, convolutional block attention module and others. Moreover, we validate that our model has great expansibility on Wine Quality Dataset and Breast Cancer Wisconsin.

ACS Style

Guoqi Chen; Wanliang Wang; Zheng Wang; Honghai Liu; Zelin Zang; Weikun Li. Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition. Applied Intelligence 2020, 50, 3503 -3520.

AMA Style

Guoqi Chen, Wanliang Wang, Zheng Wang, Honghai Liu, Zelin Zang, Weikun Li. Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition. Applied Intelligence. 2020; 50 (10):3503-3520.

Chicago/Turabian Style

Guoqi Chen; Wanliang Wang; Zheng Wang; Honghai Liu; Zelin Zang; Weikun Li. 2020. "Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition." Applied Intelligence 50, no. 10: 3503-3520.

Journal article
Published: 25 October 2019 in Sensors
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Nano-networks are composed of interconnected nano-nodes and can enable unprecedented applications in various fields. Due to the peculiarities of nano-networks, such as high density, extremely limited energy and computational resources, traditional carrier-sensing based Media Access Control (MAC) protocols are not suitable for nano-networks. In this paper, a Slot Self-Allocation based MAC protocol (SSA-MAC) is proposed for energy harvesting nano-networks. Two transmission schemes for centralized and distributed nano-networks are designed, respectively. In centralized nano-networks, nano-nodes can only send packets to the nano-controller in their Self-Allocation Slots (SASs), while, in distributed nano-networks, nano-nodes can only receive packets from surrounding nano-nodes in their SASs. Extensive simulations were conducted to compare the proposed SSA-MAC with PHysical LAyer aware MAC (PHLAME), Receiver-Initiated Harvesting-aware MAC (RIH-MAC) and Energy Efficient Wireless NanoSensor Network MAC (EEWNSN). From the results, it can be concluded that the proposed SSA-MAC achieves better performance and can reduce the collision probability, while improving the energy efficiency of nano-networks.

ACS Style

Wan-Liang Wang; Chao-Chao Wang; Xin-Wei Yao. Slot Self-Allocation Based MAC Protocol for Energy Harvesting Nano-Networks. Sensors 2019, 19, 4646 .

AMA Style

Wan-Liang Wang, Chao-Chao Wang, Xin-Wei Yao. Slot Self-Allocation Based MAC Protocol for Energy Harvesting Nano-Networks. Sensors. 2019; 19 (21):4646.

Chicago/Turabian Style

Wan-Liang Wang; Chao-Chao Wang; Xin-Wei Yao. 2019. "Slot Self-Allocation Based MAC Protocol for Energy Harvesting Nano-Networks." Sensors 19, no. 21: 4646.

Research article
Published: 10 July 2019 in Computational Intelligence and Neuroscience
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In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.

ACS Style

Zelin Zang; Wanliang Wang; Yuhang Song; Linyan Lu; Weikun Li; Yule Wang; Yanwei Zhao. Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation. Computational Intelligence and Neuroscience 2019, 2019, 1 -19.

AMA Style

Zelin Zang, Wanliang Wang, Yuhang Song, Linyan Lu, Weikun Li, Yule Wang, Yanwei Zhao. Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation. Computational Intelligence and Neuroscience. 2019; 2019 ():1-19.

Chicago/Turabian Style

Zelin Zang; Wanliang Wang; Yuhang Song; Linyan Lu; Weikun Li; Yule Wang; Yanwei Zhao. 2019. "Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation." Computational Intelligence and Neuroscience 2019, no. : 1-19.

Research article
Published: 02 May 2019 in Computational Intelligence and Neuroscience
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Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.

ACS Style

Wan Liang Wang; Weikun Li; Yu Le Wang. An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism. Computational Intelligence and Neuroscience 2019, 2019, 5126239 -27.

AMA Style

Wan Liang Wang, Weikun Li, Yu Le Wang. An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism. Computational Intelligence and Neuroscience. 2019; 2019 ():5126239-27.

Chicago/Turabian Style

Wan Liang Wang; Weikun Li; Yu Le Wang. 2019. "An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism." Computational Intelligence and Neuroscience 2019, no. : 5126239-27.

Journal article
Published: 15 March 2019 in Sustainability
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With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

ACS Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability 2019, 11, 1596 .

AMA Style

Longlong Leng, Yanwei Zhao, Zheng Wang, Jingling Zhang, Wanliang Wang, Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability. 2019; 11 (6):1596.

Chicago/Turabian Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints." Sustainability 11, no. 6: 1596.

Journal article
Published: 04 March 2019 in Neurocomputing
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Nature-inspired computing has attracted a lot of research effort especially for addressing real-world multi-objective optimization problem (MOP). This paper proposes a new nature-inspired optimization algorithm which is named opposition-based multi-objective whale optimization algorithm with global grid ranking (MOWOA). The proposed approach utilizes several parts to enhance the performance in optimization. First, the efficient evolution process is inherited from the single objective whale optimization algorithm(WOA). Second, opposition-based learning(OBL) is applied into the algorithm. Meanwhile, a novel mechanism called global grid ranking(GGR) which is inspired by grid mechanism has been incorporated into the proposed algorithm. To show the significance of the proposed algorithm, MOWOA is tested on a diverse set of benchmark with a series of well-known evolutionary algorithms and the influence of each individual strategy is also verified through 14 benchmarks. Moreover, the new proposed algorithm is also applied to the simple data clustering problem and a real-world water optimization problem in China. The results demonstrate that MOWOA is not only an algorithm with well performance for bench-mark problems but also expected to have a more wide application in real-world engineering problems.

ACS Style

Wan Liang Wang; Wei Kun Li; Zheng Wang; Li Li. Opposition-based multi-objective whale optimization algorithm with global grid ranking. Neurocomputing 2019, 341, 41 -59.

AMA Style

Wan Liang Wang, Wei Kun Li, Zheng Wang, Li Li. Opposition-based multi-objective whale optimization algorithm with global grid ranking. Neurocomputing. 2019; 341 ():41-59.

Chicago/Turabian Style

Wan Liang Wang; Wei Kun Li; Zheng Wang; Li Li. 2019. "Opposition-based multi-objective whale optimization algorithm with global grid ranking." Neurocomputing 341, no. : 41-59.

Conference paper
Published: 12 September 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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The climate policy of game theory-based understanding could be used to find some insights about how players might implement different policies. To address this issue, the cooperative climate decision-making model by using agent-based simulation and optimization is established, and the solution of the non-cooperative climate game through particle swarm optimization (PSO) is developed in this paper. Firstly, learning agents are introduced to represent several players in climate game, evolutionary strategy using the decision-making and evaluation model based on individual interests and collective interests of Nash equilibrium is proposed. Then, the nonlinear fitness function of the PSO is designed, as well as the parameter selection and analysis. Finally, the Simulation experiments are performed by the nonlinear function and compared with Genetic Algorithm (GA). Experiment results showed that the proposed algorithm in this paper achieves the expected effect with fast response ability and the model can guide all agents to make a choice rationally in the process of non-cooperative game, so that the individual benefits and collective benefits reach the Nash equilibrium.

ACS Style

Zheng Wang; Fei Wu; Wanliang Wang. PSO-Based Cooperative Strategy Simulation for Climate Game Problem. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 102 -109.

AMA Style

Zheng Wang, Fei Wu, Wanliang Wang. PSO-Based Cooperative Strategy Simulation for Climate Game Problem. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():102-109.

Chicago/Turabian Style

Zheng Wang; Fei Wu; Wanliang Wang. 2018. "PSO-Based Cooperative Strategy Simulation for Climate Game Problem." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 102-109.

Evaluation study
Published: 26 June 2018 in Computational Intelligence and Neuroscience
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Image translation, where the input image is mapped to its synthetic counterpart, is attractive in terms of wide applications in fields of computer graphics and computer vision. Despite significant progress on this problem, largely due to a surge of interest in conditional generative adversarial networks (cGANs), most of the cGAN-based approaches require supervised data, which are rarely available and expensive to provide. Instead we elaborate a common framework that is also applicable to the unsupervised cases, learning the image prior by conditioning the discriminator on unaligned targets to reduce the mapping space and improve the generation quality. Besides, domain-adversarial training inspired by domain adaptation is proposed to capture discriminative and expressive features, for the purpose of improving fidelity. Effectiveness of our method is demonstrated by compelling experimental results of our method and comparisons with several baselines. As for the generality, it could be analyzed from two perspectives: adaptation to both supervised and unsupervised setting and the diversity of tasks.

ACS Style

Zhuorong Li; Wanliang Wang; Yanwei Zhao. Image Translation by Domain-Adversarial Training. Computational Intelligence and Neuroscience 2018, 2018, 1 -11.

AMA Style

Zhuorong Li, Wanliang Wang, Yanwei Zhao. Image Translation by Domain-Adversarial Training. Computational Intelligence and Neuroscience. 2018; 2018 ():1-11.

Chicago/Turabian Style

Zhuorong Li; Wanliang Wang; Yanwei Zhao. 2018. "Image Translation by Domain-Adversarial Training." Computational Intelligence and Neuroscience 2018, no. : 1-11.

Journal article
Published: 07 August 2014 in Computer Communications
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In Wireless Local Area Networks (WLANs), the unpredictable injection of traffic load and the limitations of DCF based bandwidth allocation may result in network congestion. Also the Quality of Service (QoS) of data flows with different priorities may not be strictly guaranteed. In order to support service differentiation and guarantee QoS of data transmission over WLANs, based on the extended Lotka–Volterra (LV) biological competitive model, a bio-inspired self-adaptive rate control approach for multi-priority data transmission is proposed in this paper. This approach guides data flows to compete for network bandwidth in the way of a native ecosystem. As a result, both service differentiation and QoS can be guaranteed, simultaneously. In particular, it also achieves higher bandwidth utilization. Then the proposed approach is applied to four categories of data flows defined in the EDCA protocol, the allocated bandwidth of each category is optimized through model parameters (i.e. competition coefficients among categories) optimization. Extensive simulation studies have been conducted to show the superior performance of the proposed approach. Under the recommended model parameter values, the total bandwidth utilization is maximized up to 93% compared with 60–70% achieved by the EDCA protocol, whilst maintaining the service differentiation of multi-priority flows.

ACS Style

Xin-Wei Yao; Wan-Liang Wang; Shuang-Hua Yang; Yue-Feng Cen. Bio-inspired self-adaptive rate control for multi-priority data transmission over WLANs. Computer Communications 2014, 53, 73 -83.

AMA Style

Xin-Wei Yao, Wan-Liang Wang, Shuang-Hua Yang, Yue-Feng Cen. Bio-inspired self-adaptive rate control for multi-priority data transmission over WLANs. Computer Communications. 2014; 53 ():73-83.

Chicago/Turabian Style

Xin-Wei Yao; Wan-Liang Wang; Shuang-Hua Yang; Yue-Feng Cen. 2014. "Bio-inspired self-adaptive rate control for multi-priority data transmission over WLANs." Computer Communications 53, no. : 73-83.

Journal article
Published: 12 June 2014 in The Visual Computer
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Kernel discriminant analysis (KDA) which operates in the reproducing kernel Hilbert space (RKHS) is a very popular approach to dimensionality reduction. Kernel discriminative common vectors (KDCV) shares the same modified Fisher linear discriminant criterion with KDA and guarantees a 100 % recognition rate for the training set samples as well as favorable generalization performance. However, KDCV has the disadvantage of high computational complexity in both the training and the testing stage. This paper attempts to improve the computation efficiency of KDCV by two strategies. First, the Cholesky decomposition is introduced to obtain the projection matrix instead of eigen-decomposition. Second, we replace the matrix operation with vector operation in the testing process which reduces the computational complexity. Extensive experiments on COIL images dataset, ORL faces dataset, PIE faces dataset, and USPS handwritten digits dataset demonstrate that the proposed algorithm is more efficient than the traditional KDCV algorithm without loss of accuracy.

ACS Style

Jianwei Zheng; Qiongfang Huang; Shengyong Chen; Wanliang Wang. Efficient kernel discriminative common vectors for classification. The Visual Computer 2014, 31, 643 -655.

AMA Style

Jianwei Zheng, Qiongfang Huang, Shengyong Chen, Wanliang Wang. Efficient kernel discriminative common vectors for classification. The Visual Computer. 2014; 31 (5):643-655.

Chicago/Turabian Style

Jianwei Zheng; Qiongfang Huang; Shengyong Chen; Wanliang Wang. 2014. "Efficient kernel discriminative common vectors for classification." The Visual Computer 31, no. 5: 643-655.

Review
Published: 02 July 2012 in Sensors
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Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.

ACS Style

Hai-Yan Shi; Wan-Liang Wang; Ngai-Ming Kwok; Sheng-Yong Chen. Game Theory for Wireless Sensor Networks: A Survey. Sensors 2012, 12, 9055 -9097.

AMA Style

Hai-Yan Shi, Wan-Liang Wang, Ngai-Ming Kwok, Sheng-Yong Chen. Game Theory for Wireless Sensor Networks: A Survey. Sensors. 2012; 12 (7):9055-9097.

Chicago/Turabian Style

Hai-Yan Shi; Wan-Liang Wang; Ngai-Ming Kwok; Sheng-Yong Chen. 2012. "Game Theory for Wireless Sensor Networks: A Survey." Sensors 12, no. 7: 9055-9097.

Article
Published: 01 June 2009 in Journal of Central South University of Technology
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A novel scale-free network model based on clique (complete subgraph of random size) growth and preferential attachment was proposed. The simulations of this model were carried out. And the necessity of two evolving mechanisms of the model was verified. According to the mean-field theory, the degree distribution of this model was analyzed and computed. The degree distribution function of vertices of the generating network P(d) is 2m 2m 1−3 (d − m 1 + 1)−3, where m and m 1 denote the number of the new adding edges and the vertex number of the cliques respectively, d is the degree of the vertex, while one of cliques P(k) is 2m 2k −3, where k is the degree of the clique. The simulated and analytical results show that both the degree distributions of vertices and cliques follow the scale-free power-law distribution. The scale-free property of this model disappears in the absence of any one of the evolving mechanisms. Moreover, the randomicity of this model increases with the increment of the vertex number of the cliques.

ACS Style

Bo Wang; Xu-Hua Yang; Wan-Liang Wang. A novel scale-free network model based on clique growth. Journal of Central South University of Technology 2009, 16, 474 -477.

AMA Style

Bo Wang, Xu-Hua Yang, Wan-Liang Wang. A novel scale-free network model based on clique growth. Journal of Central South University of Technology. 2009; 16 (3):474-477.

Chicago/Turabian Style

Bo Wang; Xu-Hua Yang; Wan-Liang Wang. 2009. "A novel scale-free network model based on clique growth." Journal of Central South University of Technology 16, no. 3: 474-477.