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进化策略自适应调度的粒子群算法及应用于压力容器设计

发表时间:2012-10-31  浏览量:983  下载量:424
全部作者: 胡志敏,颜学峰
作者单位: 华东理工大学化工过程先进控制和优化技术教育部重点实验室
摘 要: 粒子群(particle swarm optimization, PSO)算法进化操作策略的各种改进或变种,将具有不同的局部或全局搜索能力。为此,提出一种多进化策略自适应调度PSO(adaptive scheduling PSO, ASPSO). ASPSO集成3种进化策略,并借鉴计算机处理器对不同进程的轮转调度算法通过对不同进化策略实施效果信息反馈,实现3种进化策略自适应轮转调度。ASPSO能根据算法不同进化阶段的进化策略实施效果反馈信息,自适应调度最佳进化策略,提高算法的全局收敛概率与局部搜索精度。通过3个典型测试函数对ASPSO性能进行测试,以及与CenterPSO,LDWPSO性能进行比较,结果表明:ASPSO的寻优性能优于CenterPSO和LDWPSO. 在压力容器设计中,ASPSO表现出了很好的寻优性与实用性。
关 键 词: 控制理论;粒子群算法;进化策略;自适应调度;压力容器
Title: Particle swarm optimization algorithm with adaptive scheduling evolution strategy and its application to pressure vessel design
Author: HU Zhimin, YAN Xuefeng
Organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology
Abstract: Considering that particle swarm optimization (PSO) with the various improvement or varieties of the evolution operating strategy has different local and global search capability, a novel adaptive scheduling PSO (ASPSO) with kinds of evolution strategies had been proposed to improve the performance of ASPSO. ASPSO algorithm employs three evolution strategies, analyzes the implementation effect of different evolution strategies, borrowing the idea of the time-sliced algorithm of the computer processor to deal with the different processes, and finally implements three evolution strategies time-sliced adaptively. According to the feedback information of the different evolution strategy implementation effect at the different evolution stages, ASPSO algorithm can schedule the best evolution strategy adaptively to make the probability of global convergence and local search precision improved. The three typical test function were employed to demonstrate the ASPSO performance, which was compared with the CenterPSO and LDWPSO. The results show that the performance of ASPSO is the best. Further, in the design of pressure vessels, ASPSO algorithm shows good performance and practicability.
Key words: control theory; particle swarm optimization algorithm; evolution strategy; adaptive scheduling; pressure vessel
发表期数: 2012年10月第20期
引用格式: 胡志敏,颜学峰. 进化策略自适应调度的粒子群算法及应用于压力容器设计[J]. 中国科技论文在线精品论文,2012,5(20):2002-2008.
 
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