Dynamic Topology Multi Force Particle Swarm Optimization Algorithm and Its Application

作     者：CHEN Dongning ZHANG Ruixing YAO Chengyu ZHAO Zheyu 

作者机构：Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao 066004 China Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University) Ministry of Education of China Qinhuangdao 066004 China Hebei Provincial Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao 066004 China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2016年第29卷第1期

页      面：124-135页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China(Grant No.51405426) Hebei Provincial Natural Science Foundation of China(Grant No.E2016203306) 

主　　题：force rule MFPSO algorithm FE topology DTMFPSO algorithm parameter optimization 

摘      要：Particle swarm optimization （PSO） algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization （DTMFPSO） algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization （MFPSO） algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing （FE） topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as gPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.