计算机工程与应用2018，Vol.54Issue(13)：131-136,6.DOI:10.3778/j.issn.1002-8331.1702-0182
基于SQP和自适应搜索的混沌粒子群算法
Chaotic particle swarm optimization algorithm based on SQP and adaptive search
摘要
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 optimi-zation problems, and the solution with respect to CPSO is more accurate and has high reliability and practical value.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Key words
particle swarm algorithm/sequential quadratic programming/chaos search/adaptive inertia weight分类
信息技术与安全科学引用本文复制引用
郑庆新,顾晓辉,张洪铭..基于SQP和自适应搜索的混沌粒子群算法[J].计算机工程与应用,2018,54(13):131-136,6.基金项目
国家科技重大专项基金(No.004040204). （No.004040204）
