基于MPI的并行多目标粒子群算法

doi:10.16451/j.cnki.issn1003-6059.201807010

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Abstract To improve the efficiency and accuracy of speed-constrained multi-objective particle swarm optimization(SMPSO), a parallelized SMPSO algorithm based on Message Passing Interface(MPI)(M-SMPSO) is proposed. The master-slave mode of MPI is used in the proposed algorithm. The entire population is divided into several sub-populations. Then, these sub-populations are evolved independently. In addition, an adaptive global optimal solution selection strategy is proposed to balance the distribution and convergence. Several standard test functions are adopted to verify the performance of the proposed algorithm. The experimental results show that ompared with other multi-objective algorithms, M-SMPSO obtains a higher speedup ratio and it converges quickly.

Key words： Multi-objective Optimization Message Passing Interface(MPI) Speed-Constrained Particle Swarm Optimization(PSO) Global Optimal Selection Strategy

Received: 10 April 2018

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61472095), Preparatory Research Foundation of Education Department of Heilongjiang Province(No.1352MSYYB016), Scientific Research Foundation of Mudanjiang Normal University(No.GP2018003)

Corresponding Authors: DONG Hongbin(Corresponding author), Ph.D., professor. His research interests include evolutionary computation, computing intelligence, data mining, multi-Agent system and machine learning.

About author:: GENG Wenjing, master student. Her research interests include multi-objective optimization and evolutionary algorithms.DING Rui, Ph.D. candidate, lecturer. Her research interests include multi-objective optimization, evolutionary algorithm and search-based software engineering.

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GENG Wenjing,DONG Hongbin,DING Rui. A Parallelized Multi-objective Particle Swarm Optimization Algorithm Based on MPI[J]. , 2018, 31(7): 668-676.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201807010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I7/668

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