Fuzzy Adaptive Binary Particle Swarm Optimization Algorithm Based on Evolutionary State Determination
LI Haojun1, ZHANG Zheng1, ZHANG Pengwei1, WANG Wanliang2
1.College of Education, Zhejiang University of Technology, Hang-zhou 310023 2.College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023
Abstract Since the binary particle swarm algorithm is easy to fall into local optimal solution and its convergence performance during later period is poor, a fuzzy adaptive binary particle swarm optimization algorithm based on evolutionary state determination(EFBPSO) is proposed. Population evolution state is determined by fuzzy classification method based on membership function. S-shaped mapping function and large inertia weight value are adopted to improve convergence speed and ensure stability of the algorithm in the earlier stage of the iterative process. V-shaped mapping function and the smaller inertia weight are employed to enhance global exploration ability of the algorithm and avoid the algorithm falling into local optimization in the later stage of iterative process. Simulation experimental results show that EFBPSO possesses higher convergence speed and accuracy and obtains better searching ability to avoid prematurity.
Fund:Supported by National Natural Science Foundation of China(No.61503340), National Social Science Foundation of China(No.16BTQ084)
Corresponding Authors:
LI Haojun(Corresponding author), Ph.D. candidate, associate professor. His research interests include intelligent computing and intelligent learning.
About author:: ZHANG Zheng, master student. His research interests include intelligent computing and intelligent learning;ZHANG Pengwei, master student. His research interests include intelligent computing and intelligent learning;WANG Wanliang, Ph.D., professor. His research interests include computer intelligent automation.
LI Haojun,ZHANG Zheng,ZHANG Pengwei等. Fuzzy Adaptive Binary Particle Swarm Optimization Algorithm Based on Evolutionary State Determination[J]. , 2018, 31(4): 358-369.
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2018, Vol. 31 
