关键词: 粒子群算法, 序列二次规划, 混沌搜索, 自适应惯性权重

Abstract: Chaotic Adaptive Particle Swarm-Sequence Quadratic Programming（CAPSO-SQP） is proposed to overcome the shortcomings of basic PSO algorithm. Based on the basic PSO algorithm, the chaos search and the adaptive inertia weight are added to improve the global convergence ability. In each iteration of the PSO algorithm, the SQP is introduced to speed up the local search and improve the whole searching effectiveness and the computational reliability of constrained optimization problems. The simulation results show that CAPSO-SQP algorithm has high accuracy, good stability and fast convergence. The cantilever structure optimization design results show the feasibility of the algorithm in structural optimization problems, and the solution with respect to CPSO is more accurate and has high reliability and practical value.

Key words: particle swarm algorithm, sequential quadratic programming, chaos search, adaptive inertia weight