膜计算多粒子群算法

doi:10.3901/JME.2019.12.222

机械工程学报 ›› 2019, Vol. 55 ›› Issue (12): 222-232.doi: 10.3901/JME.2019.12.222

• 交叉与前沿 • 上一篇

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膜计算多粒子群算法

陈东宁^1,2, 王跃颖^1,2, 姚成玉³, 刘一丹^1,2, 吕世君^1,2

1. 燕山大学河北省重型机械流体动力传输与控制重点实验室秦皇岛 066004;
2. 先进锻压成形技术与科学教育部重点实验室(燕山大学) 秦皇岛 066004;
3. 燕山大学河北省工业计算机控制工程重点实验室秦皇岛 066004

收稿日期:2018-05-15 修回日期:2018-11-16 出版日期:2019-06-20 发布日期:2019-06-20
通讯作者: 陈东宁(通信作者),女,1978年出生,博士,副教授。主要研究方向为可靠性分析及优化。E-mail:dnchen@ysu.edu.cn
作者简介:王跃颖,女,1989年出生,硕士研究生。主要研究方向为群智能优化算法及应用;姚成玉,男,1975年出生,博士后,教授。主要研究方向为系统可靠性及故障诊断;刘一丹,女,1992年出生,硕士研究生。主要研究方向为群智能优化;吕世君,男,1975年出生,博士研究生,讲师。主要研究方向为系统可靠性分析。
基金资助:
国家自然科学基金（51405426，51675460）、中国博士后科学基金（2017M621101）和河北省自然科学基金（E2016203306）资助项目

Membrane Computing Multi Particle Swarm Optimization(MC-MPSO) Algorithm

CHEN Dongning^1,2, WANG Yueying^1,2, YAO Chengyu³, LIU Yidan^1,2, Lü Shijun^1,2

1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004;
2. Key Laboratory of Advanced Forging & Stamping Technology and Science(Yanshan University), Ministry of Education of China, Qinhuangdao 066004;
3. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004

Received:2018-05-15 Revised:2018-11-16 Online:2019-06-20 Published:2019-06-20

摘要/Abstract

摘要： 针对粒子群（Particle swam optimization，PSO）算法进化后期收敛速度较慢，易陷入局部极值点，精度较差等不足，提出膜计算多粒子群（MC-MPSO）算法。在该算法中，将原始PSO、标准PSO、中值导向粒子群（MPSO）、扩展粒子群（EPSO）、多作用力粒子群（MFPSO）、两阶段作用力粒子群（TFPSO）等六种具有不同优点的粒子群算法分别放入六个基本膜内，提出MC-MPSO算法的膜间交流与粒子更新机制，在进化前期，各粒子群算法按自身机制进行搜索寻优，即各基本膜各自进化来充分发挥各基本膜内算法的优点；在进化后期，各基本膜内算法与比自身更好的表层膜内最优解粒子交流，各表层膜逐步吞并搜索能力较差的基本膜，而最适合问题优化求解的基本膜长大并按照表层膜输出，使MC-MPSO算法集成了基本膜内六种粒子群算法的各自优势，并具有适应不同类型优化求解问题的寻优能力。通过与基本膜内六种粒子群算法的测试对比，与遗传算法、鱼群算法及其他基于膜计算的粒子群算法的比较，证明了MC-MPSO算法具有更好的寻优能力和适用性。最后，将MC-MPSO算法应用于串联和桥式系统可靠性优化问题，验证了所提算法的有效性。

关键词: MC-MPSO算法, 可靠性优化, 粒子群算法, 膜计算

Abstract: Membrane computing multi particle swarm optimization(MC-MPSO) algorithm is proposed to overcome of particle swarm opmtimization(PSO) algorithm the defections of easy getting trapped in a local optimum, slow convergent speed and low accuracy in the later evolution process. In MC-MPSO algorithm, original PSO, standard PSO, median-oriented PSO(MPSO), extended PSO(EPSO), multi force PSO(MFPSO), two-stage force PSO(TFPSO) with different advantages of particle swarm algorithm are put into six membranes respectively. The communication among membranes and update mechanisms of particles are proposed in MC-MPSO. First of all, elementary membrane grow up according to their own searching mechanism with the advantages of each PSO algorithm. Secondly, six algorithms in the membranes exchange optimally with better membrane, and the surface membrane gradually swallow the membranes of poor searching ability. Then the membranes which can solve the problems properly grow up and the best membrane export through surface membrane. The MC-MPSO algorithm integrates the advantages of the six particle swarm optimization algorithms, and has the ability to adapt to different types of optimization problems. By comparing with the test of six algorithms in the membranes, the comparison of genetic algorithm, fish swarm algorithm and other particle swarm optimization algorithms based on membrane computing, the results show that the MC-MPSO algorithm has better search capability of optimal solution and wide applicability. Finally, the MC-MPSO algorithm is applied in the reliability optimization of series and bridge systems. The effectiveness of the proposed algorithm is verified.

Key words: MC-MPSO algorithm, membrane computing, PSO algorithm, reliability optimization

中图分类号:

TP18
TB114

陈东宁, 王跃颖, 姚成玉, 刘一丹, 吕世君. 膜计算多粒子群算法[J]. 机械工程学报, 2019, 55(12): 222-232.

CHEN Dongning, WANG Yueying, YAO Chengyu, LIU Yidan, Lü Shijun. Membrane Computing Multi Particle Swarm Optimization(MC-MPSO) Algorithm[J]. Journal of Mechanical Engineering, 2019, 55(12): 222-232.

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膜计算多粒子群算法

Membrane Computing Multi Particle Swarm Optimization(MC-MPSO) Algorithm

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