Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2369-2375,2387

Multi-swarm collaborative particle swarm optimization algorithm based on comprehensive dimensional learning

Zhang Qiwen
Wang Yangting
School of Computer & Communication, Lanzhou University of Technology, Lanzhou 730050, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0019

Abstract

Aiming at the problem of over-exploitation in dimensional learning strategy(DLS), this paper proposed a multi-swarm collaborative particle swarm optimization algorithm with comprehensive dimension learning(CDL-MCPSO). The cluster structure of master-slave paradigm divided the population into a master group and four slave groups, in which the master group executed a comprehensive learning strategy to conduct a large-scale exploration in the search space, and the slave groups executed a comprehensive dimensional learning strategy(CDL) to exploit high-precision solutions near the local optimal. The master-slave groups could effectively achieve the balance between exploration and exploitation by executing algorithms with different functions. At the same time, in order to maintain the diversity of the population, the CDL-MCPSO proposed a new solution exchange mechanism(SEM). It could accomplish information exchange and cooperation after the master-slave groups running their respective algorithms for several generations independently, so as to guide the particles to conduct more accurate searches in the later stage. Finally, for the high randomness of the initialization process, the algorithm adopted Latin hypercube sampling to reconstruct the input distribution. In order to verify the effectiveness of CDL-MCPSO, compared with 5 kinds of PSO variants in 10 test functions. The results show that the algorithm can always find better or equivalent solutions. It is feasible and efficient in solving complex functions.

Foundation Support

国家自然科学基金资助项目(62063021,62162040)
浙江省基础公益研究计划资助项目(LQ20F020011)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0019
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2369-2375,2387
Serial Number: 1001-3695(2022)08-022-2369-07

Publish History

[2022-03-22] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

张其文, 王杨婷. 综合维度学习的多群协作粒子群优化算法 [J]. 计算机应用研究, 2022, 39 (8): 2369-2375,2387. (Zhang Qiwen, Wang Yangting. Multi-swarm collaborative particle swarm optimization algorithm based on comprehensive dimensional learning [J]. Application Research of Computers, 2022, 39 (8): 2369-2375,2387. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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