Multiswarm Particle Swarm Optimization with Transfer of the Best Particle.
We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
Xiao-peng
XP
Wei
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
Jian-xia
JX
Zhang
Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China.
Dong-sheng
DS
Zhou
Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China.
Qiang
Q
Zhang
D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical