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<p>The optical microscope algorithm (OMA) has garnered significant attention due to its clear mechanism and effectiveness in solving various optimization problems. However, the approach demonstrates vulnerabilities to premature convergence and local solution attraction, particularly in complex, high-dimensional problem spaces. Therefore, this paper proposes OMA with a precise focusing strategy and migration strategy (PMOMA). Firstly, a multi-fusion strategy will be proposed to enhance the initial population quality. Secondly, an introduction of the precise focusing strategy enhances the exploration ability of the algorithm and accelerates convergence speed. Finally, introducing a migration strategy prevents the algorithm from falling into local optima, resulting in improved solution accuracy. To evaluate PMOMA’s performance, we compare it with 9 other algorithms in CEC 2017 benchmark functions, Virtual Library of Simulation Experiments, and NLtoolbox. Additionally, PMOMA was implemented for solving engineering applications and&#xa0;3D path planning to further validate its effectiveness. Overall, PMOMA demonstrated superiority in both numerical benchmarks and practical experiments.</p>

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An optical microscope algorithm with precise focusing strategy and migration strategy with application in 3D path planning

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Enhui Dai,
  • Zhenxue He,
  • Xiaojun Zhao,
  • Xiaodan Zhang,
  • Yijin Wang,
  • Xiang Wang

摘要

The optical microscope algorithm (OMA) has garnered significant attention due to its clear mechanism and effectiveness in solving various optimization problems. However, the approach demonstrates vulnerabilities to premature convergence and local solution attraction, particularly in complex, high-dimensional problem spaces. Therefore, this paper proposes OMA with a precise focusing strategy and migration strategy (PMOMA). Firstly, a multi-fusion strategy will be proposed to enhance the initial population quality. Secondly, an introduction of the precise focusing strategy enhances the exploration ability of the algorithm and accelerates convergence speed. Finally, introducing a migration strategy prevents the algorithm from falling into local optima, resulting in improved solution accuracy. To evaluate PMOMA’s performance, we compare it with 9 other algorithms in CEC 2017 benchmark functions, Virtual Library of Simulation Experiments, and NLtoolbox. Additionally, PMOMA was implemented for solving engineering applications and 3D path planning to further validate its effectiveness. Overall, PMOMA demonstrated superiority in both numerical benchmarks and practical experiments.