融合多策略改进的黏菌优化算法

doi:10.16451/j.cnki.issn1003-6059.202307006

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Abstract

The slime mould algorithm(SMA) yields low convergence efficiency and is easily dropped into local shackles. To address these shortcomings, an improved slime mould algorithm fused with multiple strategies(MISMA) is proposed.The Halton sequence is introduced to enrich the diversity of the initial population, and consequently the ergodicity and convergence precision of the algorithm are improved.The differential variation idea is employed to modify the global position update equations and enhance the global exploration capability and the continuous optimization performance.The local search strategy combining convergence factor and elite selection mechanism is ameliorated to improve the local development capacity of the algorithm. Thus, the global search performance and local exploitation capability of the algorithm are well balanced. The lens imaging learning strategy based on dynamic boundary is proposed to improve the individual quality and the capability of the algorithm avoiding prematurity and getting rid of local constraints.Numerical simulation experiments of 13 benchmark functions and some CEC2014 test functions show the strong robustness of MISMA.Moreover, the superiority and applicability of MISMA for dealing with practical engineering optimization problems are verified through parameter optimization experiments of the PV module model.

Key words： Slime Mould Algorithm(SMA) Halton Sequence Differential Mutation Convergence Factor Lens Imaging Learning Dynamic Boundary

Received: 12 May 2023

ZTFLH:

TP301.6

Fund:

National Natural Science Foundation of China(No.51665025)

Corresponding Authors: ZHANG Changsheng, master, associate professor. His research interests include complex industrial process modeling and intelligent optimization algorithms.

About author:: LI Dekai, master student. His research interests include intelligent optimization algorithms and image processing.YANG Xuesong, master student. His research interests include image processing and intelligent optimization algorithms.

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	LI Dekai
	ZHANG Changsheng
	YANG Xuesong

Cite this article:

LI Dekai,ZHANG Changsheng,YANG Xuesong. Improved Slime Mould Algorithm Fused with Multi-strategy[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 647-660.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202307006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I7/647