Global Optimization Method Based on Consensus Particle Swarm Optimization

doi:10.16182/j.issn1004731x.joss.20-FZ0371

Abstract

Abstract: According to the characteristics of particle swarm optimization (PSO) and efficient global optimization algorithm (EGO), a global black box optimization algorithm based on consensus particle swarm optimization and local surrogate model (CPSO-LSM) is proposed. The algorithm fixes the period of the PSO algorithm to group the particles and stops after the particles reach a consensus. The high-quality sub-regions around each group of particles are used as the modeling area of the surrogate model, and the high-quality optimal solution or global optimal solution is obtained by comparing the optimal values of each region. It can not only avoid the complex calculation of PSO, improve the speed and precision of establishing agent model, but also avoid falling into local optimum. By comparing the simulation results of other algorithms in standard test functions, CPSO-LSM has better convergence speed and solution accuracy.

Key words: particle swarm optimization, efficient global optimization algorithm, consensus-based PSO, surrogate model

CLC Number:

TP301.6

Lu Zhanwen, Cheng Xingong, Zhang Yongfeng. Global Optimization Method Based on Consensus Particle Swarm Optimization[J]. Journal of System Simulation, 2020, 32(10): 1936-1942.

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