The Experts below are selected from a list of 7383 Experts worldwide ranked by ideXlab platform
Yuhui Shi - One of the best experts on this subject based on the ideXlab platform.
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Adaptive online data-driven closed-loop parameter control strategy for Swarm Intelligence Algorithm
Information Sciences, 2020Co-Authors: Yaxian Liu, Shi Cheng, Yuhui ShiAbstract:Abstract Parameter control is critical for the performance of any Swarm Intelligence Algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a Swarm Intelligence Algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy involves three key parts: controller design, feedback selection, and reference determination. First, based on the control theory, we adopt a proportional integral derivative (PID) controller in the CLPC strategy, which can adaptively adjust the value of parameter according to the difference between reference and feedback. Second, to reflect and monitor the evolution state in real time, we use the mean shift clustering method and define the convergence entropy and the extension entropy to generate feedback. Finally, the reference should provide useful guidance for parameter control according to the features of optimization problems. Thus, in single-objective optimization, we propose a new lossless fitness landscape analysis method and design a decision tree to determine the reference; in multi-objective optimization, the range of the convergence entropy and the extension entropy are regarded as the reference. In addition, to illustrate the effectiveness of the CLPC strategy, two groups of experiments are performed based on the particle Swarm optimization (PSO) Algorithm in single-objective and multi-objective optimization. At the optimization Algorithm level, we compare our proposed CLPC-PSO Algorithm with three standard PSO Algorithms, three decreasing inertia weight PSO Algorithms, and two adaptive PSO Algorithms. At the optimization problem level, we perform abundant experiments based on five single-objective benchmark functions, five multi-objective benchmark functions, and twelve scheduling instances. The statistical results show that the performance of the proposed CLPC-PSO Algorithm is considerably better and more stable than those of the other eight PSO variants when faced with different problems having various features.
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A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows
IEEE Access, 2020Co-Authors: Yang Shen, Yuhui Shi, Mingde Liu, Jian Yang, Martin MiddendorfAbstract:The Vehicle Routing Problem with Time Windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid Swarm Intelligence Algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) Algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed Algorithm.
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Unified Swarm Intelligence Algorithms
Critical Developments and Applications of Swarm Intelligence, 2018Co-Authors: Yuhui ShiAbstract:In this chapter, the necessity of having developmental learning embedded in a Swarm Intelligence Algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several Swarm Intelligence Algorithms are looked at from a developmental learning perspective. A framework of a developmental Swarm Intelligence Algorithm, which contains capacity developing stage and capability learning stage, is further given to help understand developmental Swarm Intelligence (DSI) Algorithms, and to guide to design and/or implement any new developmental Swarm Intelligence Algorithm and/or any developmental evolutionary Algorithm. Following DSI, innovation is discussed and an innovation-inspired optimization (IO) Algorithm is designed and developed. Finally, by combing the DSI and IO Algorithm together, a unified Swarm Intelligence Algorithm is proposed, which contains capacity developing stage and capability learning stage and with three search operators in its capability learning stage to mimic the three levels of innovations.
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CEC - A comprehensive survey of brain storm optimization Algorithms
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiujuan Lei, Yuhui ShiAbstract:The development, implementation, variant, and future directions of a new Swarm Intelligence Algorithm, brain storm optimization (BSO) Algorithm, are comprehensively surveyed. Brain storm optimization Algorithm is a new and promising Swarm Intelligence Algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. To the best of our knowledge, there are 75 papers, 8 theses, and 5 patents in total on the development and application of the BSO Algorithm. Every individual in the BSO Algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Based on the developments of brain storm optimization Algorithms, different kinds of optimization problems and real-world applications could be solved.
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ICACI - Brain storm optimization with chaotic operation
2015 Seventh International Conference on Advanced Computational Intelligence (ICACI), 2015Co-Authors: Zhensu Yang, Yuhui ShiAbstract:Brain storm optimization (BSO) Algorithm is a Swarm Intelligence Algorithm inspired by the brainstorming process. In this paper, a modified BSO which is BSO with chaotic operation (BSOCO) is proposed to solve the premature problem of the original BSO. Chaos has the properties of ergodicity, intrinsic stochastic property and sensitive to initial conditions, therefore the proposed BSO is expected to have better performance at avoiding being trapped into local optima than the original BSO, which is illustrated by experimental results on benchmark functions.
Martin Middendorf - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows
IEEE Access, 2020Co-Authors: Yang Shen, Yuhui Shi, Mingde Liu, Jian Yang, Martin MiddendorfAbstract:The Vehicle Routing Problem with Time Windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid Swarm Intelligence Algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) Algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed Algorithm.
Yongxu Zhao - One of the best experts on this subject based on the ideXlab platform.
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millimeter wave microstrip antenna design based on Swarm Intelligence Algorithm in 5g
Global Communications Conference, 2017Co-Authors: Rongling Jian, Yueyun Chen, Yuanyang Cheng, Yongxu ZhaoAbstract:In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization Algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.
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GLOBECOM Workshops - Millimeter Wave Microstrip Antenna Design Based on Swarm Intelligence Algorithm in 5G
2017 IEEE Globecom Workshops (GC Wkshps), 2017Co-Authors: Rongling Jian, Yueyun Chen, Yuanyang Cheng, Yongxu ZhaoAbstract:In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization Algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.
Shi Cheng - One of the best experts on this subject based on the ideXlab platform.
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Adaptive online data-driven closed-loop parameter control strategy for Swarm Intelligence Algorithm
Information Sciences, 2020Co-Authors: Yaxian Liu, Shi Cheng, Yuhui ShiAbstract:Abstract Parameter control is critical for the performance of any Swarm Intelligence Algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a Swarm Intelligence Algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy involves three key parts: controller design, feedback selection, and reference determination. First, based on the control theory, we adopt a proportional integral derivative (PID) controller in the CLPC strategy, which can adaptively adjust the value of parameter according to the difference between reference and feedback. Second, to reflect and monitor the evolution state in real time, we use the mean shift clustering method and define the convergence entropy and the extension entropy to generate feedback. Finally, the reference should provide useful guidance for parameter control according to the features of optimization problems. Thus, in single-objective optimization, we propose a new lossless fitness landscape analysis method and design a decision tree to determine the reference; in multi-objective optimization, the range of the convergence entropy and the extension entropy are regarded as the reference. In addition, to illustrate the effectiveness of the CLPC strategy, two groups of experiments are performed based on the particle Swarm optimization (PSO) Algorithm in single-objective and multi-objective optimization. At the optimization Algorithm level, we compare our proposed CLPC-PSO Algorithm with three standard PSO Algorithms, three decreasing inertia weight PSO Algorithms, and two adaptive PSO Algorithms. At the optimization problem level, we perform abundant experiments based on five single-objective benchmark functions, five multi-objective benchmark functions, and twelve scheduling instances. The statistical results show that the performance of the proposed CLPC-PSO Algorithm is considerably better and more stable than those of the other eight PSO variants when faced with different problems having various features.
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CEC - A comprehensive survey of brain storm optimization Algorithms
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiujuan Lei, Yuhui ShiAbstract:The development, implementation, variant, and future directions of a new Swarm Intelligence Algorithm, brain storm optimization (BSO) Algorithm, are comprehensively surveyed. Brain storm optimization Algorithm is a new and promising Swarm Intelligence Algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. To the best of our knowledge, there are 75 papers, 8 theses, and 5 patents in total on the development and application of the BSO Algorithm. Every individual in the BSO Algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Based on the developments of brain storm optimization Algorithms, different kinds of optimization problems and real-world applications could be solved.
Wang Jing - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy multi-objective software reliability redundancy allocation based on Swarm Intelligence Algorithm
Journal of Computer Applications, 2013Co-Authors: Wang JingAbstract:A fuzzy multi-objective software reliability allocation model was established,and bacteria foraging optimization Algorithm based on estimation of distribution was proposed to solve software reliability redundancy allocation problem.As the fuzzy target function,software reliability and cost were regarded as triangular fuzzy members,and bacterial foraging Algorithm optimization based on Gauss distribution was applied.Different membership function parameters were set up,and different Pareto optimal solutions could be obtained.The experimental results show that the proposed Swarm Intelligence Algorithm can solve multi-objective software reliability allocation effectively and correctly,Pareto optimal solution can help the decision between software reliability and cost.
