混合多策略改进的蜣螂优化算法  

Improved Dung Beetle Optimization Algorithm by Hybrid Multi-Strategy

作　　者：娄革伟 郑永煌[1] 陈均[1] 谌廷政[1] 索相波[1] 刘旭亮 LOU Gewei;ZHENG Yonghuang;CHEN Jun;SHEN Tingzheng;SUO Xiangbo;LIU Xuliang(Jiuquan Satellite Launch Center,Jiuquan,Gansu 735700,China)

机构地区：[1]酒泉卫星发射中心,甘肃酒泉735700

出　　处：《计算机工程与应用》2024年第24期97-109,共13页Computer Engineering and Applications

基　　金：航天智能自主发射技术试验验证项目。

摘　　要：针对原始蜣螂优化算法全局探索能力不足、易陷入局部最优以及收敛精度不理想等问题,提出了一种混合多策略改进的蜣螂优化算法。采用混沌映射结合随机反向学习策略初始化种群提高多样性,扩大解空间搜索范围,增强全局寻优能力;通过黄金正弦策略实现个体动态搜索,提高算法遍历性;引入竞争机制增强信息交互,平衡全局探索与局部开发,加快算法收敛速度;最后在迭代后期利用自适应t分布变异对个体进行扰动,避免算法陷入局部最优。在23个基准测试函数中,将该算法与其他优化算法进行对比测试,结果表明,改进后的算法具有更强的寻优性能、更高的收敛精度和更好的稳定性。在具体工程设计实例中的应用验证了该算法在处理实际优化问题上的有效性。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 pro-posed 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 applica-tion of the proposed algorithm in engineering design examples demonstrate its effectiveness in dealing with real optimiza-tion problems.

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 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]