•  
  •  
 

Journal of System Simulation

Abstract

Abstract: For the current algorithm, it is difficult to obtain the available solution due to the irregularity of problem decision space caused by the numerous mixed variable optimization problems during real industrial applications. The coevolution strategy is introduced and a mixed variable particle swarm optimization algorithm(CCPSO) based on competitive coevolution is proposed. The search direction adjustment mechanism based on tolerance is designed to judge the evolution state of particles, adaptively adjust the search direction of particles, avoid falling into local optimum, and balance the convergence and diversity of the population. The learning object generation mechanism is adopted for each particle to generate new learning objects when particle evolution stagnation is detected to promote the evolution of particles and improve the diversity of the population. The prediction strategy based on competitive learning is applied to select the appropriate learning objects for particles, which makes full use of the learning potential of new and old learning objects and ensures the convergence speed of the algorithm. Experimental results show that, CCPSO can obtain the better results compared with the other main mixed variable optimization algorithms.

First Page

844

Last Page

858

CLC

TP273

Recommended Citation

Zhang Hu, Zhang Heng, Huang Zilu, et al. Mixed-variable Particle Swarm Optimization Algorithm Based on Competitive Coevolution[J]. Journal of System Simulation, 2024, 36(4): 844-858.

DOI

10.16182/j.issn1004731x.joss.22-1466

Download

Share

COinS