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Application of hybrid PSO-GA algorithm in optimization of high-dimensional complex functions

Published: 22 May 2022 Publication History

Abstract

To improve the optimization of high-dimensional complex functions,In this paper,we combine both GA and PSO to propose an improved hybrid PSO-GA algorithm.First,the learning factors and inertial weights of the first half PSO are modified in the improved algorithm to optimize the local and global search.An adaptive GA is then introduced in the second half of the algorithm to balance population diversity and avoid falling into local optimal.Finally,this paper uses four typical test functions,performing a testing and comparative analysis of the algorithm.Experimental results show that the improved hybrid algorithm can not only effectively avoid the local optimum,but also improve the optimization ability of the function.

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ICMIP '22: Proceedings of the 2022 7th International Conference on Multimedia and Image Processing
January 2022
250 pages
ISBN:9781450387408
DOI:10.1145/3517077
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2022

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Author Tags

  1. GA
  2. PSO
  3. adaptive
  4. algorithm optimization

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • National Natural Science Foundation of China

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ICMIP 2022

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