改进的矮猫鼬优化算法求解约束优化问题

doi:10.3778/j.issn.1002-8331.2311-0164

摘要/Abstract

摘要： 为提高矮猫鼬优化算法在求解约束优化问题的寻优性能，提出一种改进的矮猫鼬优化算法(D_PCDMO)。基于矮猫鼬的生活习性，修改算法中的窥视行为，以更好模拟矮猫鼬的觅食行为；提出一种候选解更新机制，以增强算法的勘探能力，提高算法寻优性能；构造一种新的动态惩罚因子，以提升求解约束优化问题的寻优能力。通过CEC2019基准测试函数和CEC2017约束优化基准测试函数与其他算法的数值对比及4个工程优化问题的求解，实验结果表明，相比于其他对比算法，D_PCDMO算法具有收敛精度高与收敛速度快等优势，且能有效地解决复杂的工程优化问题，具有较强的竞争力。

关键词: 约束优化, 矮猫鼬优化算法, 窥视行为, 候选解更新机制, 动态惩罚因子

Abstract: To improve the optimization performance of dwarf mongoose optimization algorithm in solving constrained optimization problems, an improved dwarf mongoose algorithm (D_PCDMO) is proposed. Firstly, based on the living habits of mongoose, the peep behavior in the algorithm is modified to better simulate the foraging behavior of mongoose. Secondly, a candidate solution location updating mechanism is proposed to enhance the exploration ability of the algorithm and improve the optimization performance of the algorithm. Finally, a new dynamic penalty factor is constructed to improve the optimization ability of solving constrained optimization problems. Through numerical comparisons with other algorithms on the CEC2019 benchmark function and the CEC2017 constraint optimization benchmark function, and by solving four engineering optimization problems, this paper has demonstrated the effectiveness of this approach in such scenarios. Numerical experimental results show that, compared with other comparative algorithms, the D_PCDMO algorithm has the advantages of high convergence accuracy and fast convergence speed, and can effectively solve complex engineering optimization problems, and has stronger competitiveness.

Key words: constrained optimization, dwarf mongoose optimization algorithm, peep behavior, update mechanism of candidate solution, dynamic penalty factor

陈淼, 崔倩倩, 赵秋丽, 赵世杰. 改进的矮猫鼬优化算法求解约束优化问题[J]. 计算机工程与应用, 2025, 61(8): 351-362.

CHEN Miao, CUI Qianqian, ZHAO Qiuli, ZHAO Shijie. Improved Dwarf Mongoose Optimization Algorithm for Solving Constrained Optimization Problems[J]. Computer Engineering and Applications, 2025, 61(8): 351-362.

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