Multi-strategy improved optical microscope algorithm based on periodic mutation and encircling mechanism

doi:10.19682/j.cnki.1005-8885.2025.0003

中国邮电高校学报(英文) ›› 2025, Vol. 32 ›› Issue (1): 31-60.doi: 10.19682/j.cnki.1005-8885.2025.0003

• Artificial Intelligence • 上一篇下一篇

Multi-strategy improved optical microscope algorithm based on periodic mutation and encircling mechanism

吴进¹,苏正东¹,王辉立¹,²,苏泽林¹,²

1. 西安邮电大学
2.

收稿日期:2024-01-30 修回日期:2024-09-12 出版日期:2025-02-28 发布日期:2025-02-28
通讯作者: 吴进 E-mail:wujin1026@126.com
基金资助:
科技创新2030——“新一代人工智能”重大项目“面向复杂场景的自重构自演化AI芯片研制及应用”

Multi-strategy improved optical microscope algorithm based on periodic mutation and encircling mechanism

Received:2024-01-30 Revised:2024-09-12 Online:2025-02-28 Published:2025-02-28
Contact: Jin WU E-mail:wujin1026@126.com
Supported by:
National Key R&D Program of China: Science and Technology Innovation 2030 – “New Generation Artificial Intelligence” Major Project “Development and Application of Self-Reconstructing and Self-Evolving AI Chips for Complex Scenarios”

摘要/Abstract

摘要：

Aiming at the problems of poor initial population quality, slow convergence, and long-running time of optical microscope algorithm ( OMA), a multiple-strategy improved OMA based on periodical variation and encircling mechanism, called MOMA, was proposed in this paper. Firstly, the good point set population initialization is introduced to obtain a uniform initial population. Secondly, the periodic mutation and encircling mechanism are successively used to improve the convergence speed. Finally, the MOMA’s running time is optimized by introducing the conversion factor and the corresponding threshold, while balancing the exploration and exploitation. Experimental and analytical comparisons are made with OMA and 7 other excellent optimizers on 21 benchmark functions. The results show that MOMA largely outperforms the original algorithm. Furthermore, by applying MOMA to the optimization experiments of the no-wait flow-shop scheduling problem ( NWFSP), MOMA can obtain the optimal completion time and the fastest convergence speed compared to modified particle swarm optimization ( PSO) using adaptive strategy, grey wolf optimizer ( GWO), golden jackal optimization ( GJO), and OMA.

关键词:

good point set, periodic mutation, conversion factor, encircling mechanism, scheduling optimization

Abstract:

Key words: good point set, periodic mutation, conversion factor, encircling mechanism, scheduling optimization

中图分类号:

TP301.6

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Multi-strategy improved optical microscope algorithm based on periodic mutation and encircling mechanism

Multi-strategy improved optical microscope algorithm based on periodic mutation and encircling mechanism

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