基于多种群细菌觅食算法的机械设计优化

doi:10.16731/j.cnki.1671-3133.2017.09.025

现代制造工程 ›› 2017, Vol. 444 ›› Issue (9): 144-149.doi: 10.16731/j.cnki.1671-3133.2017.09.025

• 设备设计/诊断维修/再制造 • 上一篇下一篇

基于多种群细菌觅食算法的机械设计优化

晏晓辉¹, 张智聪¹, 郭建文¹, 马连博²

1 东莞理工学院机械工程学院,东莞 523808;
2 东北大学,沈阳 110819

收稿日期:2016-02-05 出版日期:2017-09-20 发布日期:2018-01-09
作者简介:晏晓辉,博士,讲师,主要研究方向为智能优化方法、企业信息化。张智聪,博士,教授,研究方向为调度优化。郭建文,博士,讲师,研究方向为机械工程。马连博,博士,副研究员,研究方向为智能优化计算。E-mail:yanxh@dgut.edu.cn
基金资助:
国家自然科学基金项目(61503373,61703102);广东省自然科学基金项目(2015A030310274, 2015A030313649);广东省教育科学规划项目(14JXN051);广东省高等学校创新强校工程项目(2015GXJK144);东莞市人文社科项目(201450714000092);广东省科技计划项目 (2015A010103021);广东省教育厅2015年重点平台及科研项目 (2015KTSCX137)

Mechanical design and optimization based on multi-swarm bacterial foraging optimization

Yan Xiaohui¹, Zhang Zhicong¹, Guo Jianwen¹, Ma Lianbo²

1 School of Mechanical Engineering,Dongguan University of Technology,Dongguan 523808,Guangdong,China;
2 School of Software, Northeastern University,Shenyang 110819,China

Received:2016-02-05 Online:2017-09-20 Published:2018-01-09

摘要/Abstract

摘要： 针对传统方法在解决机械优化问题时的不足,提出了一种偏利共生型的多种群细菌觅食算法。利用可行种群和不可行种群两个种群来模拟协同演化,以用于机械设计优化问题的多约束处理。除多种群协同进化外,该算法还采用了自适应的趋化步长,以及均衡的繁殖消亡策略。为验证所提出算法的优化设计能力,在压力容器和焊接悬臂梁优化设计两个实例上进行了测试,并与其他几个算法进行了比较。测试结果表明所提出的多种群细菌觅食算法在最优值以及稳定性上相比于同类算法具有明显优势,能够很好地用于机械设计优化工作。

关键词: 机械设计, 约束优化, 多种群细菌觅食算法, 协同进化

Abstract: To overcome the disadvantage of traditional methods in solving the problem of mechanical optimization,a Multi-Swarm Bacterial Foraging Optimization (MSBFO) algorithm was proposed,which uses the idea of commensalism in nature.In the algorithm,two swarms were used to represent the feasible population and infeasible population,to simulate the co-evolution,in order to solve the constrained mechanical design optimization problem.Adaptive step lengths in chemotaxis and equilibrium strategy in reproduction are also used in MSBFO.To validate the optimization design ability of proposed algorithm,tested it on instances of pressure vessel design and welded beam optimization design,and compared its results with several other algorithms.Test results show that the proposed MSBFO algorithm outperforms most comparison algorithms both on the optimal value and the stability of the results,and can be used for mechanical design optimization very well.

Key words: mechanical design, constrained optimization, Multi-Swarm Bacterial Foraging Optimization(MSBFO), coevolution

中图分类号:

TP391

晏晓辉, 张智聪, 郭建文, 马连博. 基于多种群细菌觅食算法的机械设计优化[J]. 现代制造工程, 2017, 444(9): 144-149.

Yan Xiaohui, Zhang Zhicong, Guo Jianwen, Ma Lianbo. Mechanical design and optimization based on multi-swarm bacterial foraging optimization[J]. Modern Manufacturing Engineering, 2017, 444(9): 144-149.

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基于多种群细菌觅食算法的机械设计优化

Mechanical design and optimization based on multi-swarm bacterial foraging optimization

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