| Citation: | HE J W，XU X Z，GAO B. Improved moth-flame optimization algorithm with multi-strategy integration[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2862-2871 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0707
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An enhanced moth-flame optimization algorithm with multi-strategy integration (RGMFO) was proposed in order to address the issues that the moth-flame optimization algorithm was prone to falling into the local optimum and that its exploitation ability was insufficient while it was approaching the global optimum. To generate high-quality moth populations, a random opposition-based learning strategy was applied at the start of each iteration. To generate high-quality moth populations, a random opposition-based learning strategy was applied at the start of each iteration. Gaussian mutation was then used to swap out subpar flame individuals with superior ones. Archimedes spirals, weighting factors, and the surrounding of a small number of optimal flames were employed to enhance the moth update mechanism. The proposed algorithm was tested on 11 benchmark functions of different types. The test results and rank sum test show that RGMFO has better global search ability and higher search accuracy. Lastly, RGMFO is applied to the engineering scenarios of reducer design and trough bulkhead design, which further verifies the practicability and feasibility of the algorithm.
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