关键词: 蜣螂优化算法, 随机反向学习, 混沌映射, 黄金正弦策略, 竞争机制, [t]分布变异, 基准测试函数, 工程设计实例

Abstract: An improved dung beetle optimization algorithm using hybrid multi-strategy is proposed, to make up for the shortcomings of the original dung beetle optimization algorithm, such as insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. The chaotic mapping and random opposition-based learning are used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. The golden sine strategy is applied to facilitate individual dynamic search and enhance the ergodicity of algorithm. The introduction of competitive mechanism enhances information exchange, balances global exploration with local development, and accelerates the convergence speed of algorithm. In the late iterations, the adaptive [t]-distribution mutation is introduced to provide perturbation and avoid falling into local optimization. The proposed algorithm is compared with other optimization algorithms by 23 benchmark test functions. The results show that the improved algorithm has stronger optimization performance, higher convergence accuracy and better stability. The application of the proposed algorithm in engineering design examples demonstrate its effectiveness in dealing with real optimization problems.

Key words: dung beetle optimization algorithm, random opposition-based learning, chaotic mapping, golden sine strategy, competitive mechanism, [t]-distribution mutation, benchmark test function, engineering design example