Hu Wei, Zhang Qi, Ye Shan
Panzhihua University, Panzhihua, China.
Chengdu Technological University, Chengdu, China.
Sci Rep. 2025 Feb 7;15(1):4655. doi: 10.1038/s41598-025-88347-z.
In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimization algorithm using hybrid multi- strategy is proposed. Firstly, the cubic chaotic mapping approach is used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. Secondly, the cooperative search algorithm is utilized to strength communication between individual dung beetles and dung beetle groups in foraging stage to expand the search range of the solution space and enhance the global optimization ability. Thirdly, T-distribution mutation and differential evolutionary variation strategies are introduced to provide perturbation to enhance the diversity of the population and avoid falling into local optimization. Fourthly, the proposed algorithm(named as SSTDBO) is compared with other optimization algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA and HHO, by 29 benchmark test functions in CEC2017. The results show that the proposed algorithm has stronger robustness and optimization ability, and algorithm's performance has substantially enhanced. Finally, the proposed algorithm is applied to solve the real-world robot path planning engineering cases, to demonstrate its effectiveness in dealing with real optimization engineering cases, which further verified how noteworthy the enhanced strategy's efficacy and the enhanced algorithm's superiority are in addressing real-world engineering cases.
为了弥补原始蜣螂优化算法的不足，如种群多样性低、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，提出了一种基于混合多策略的改进蜣螂优化算法。首先，采用三次混沌映射方法初始化种群，以提高多样性，扩大解空间的搜索范围，增强全局优化能力。其次，利用协同搜索算法加强个体蜣螂与蜣螂群体在觅食阶段的通信，扩大解空间的搜索范围，增强全局优化能力。第三，引入T分布变异和差分进化变异策略提供扰动，增强种群多样性，避免陷入局部最优。第四，通过CEC2017中的29个基准测试函数，将所提出的算法（命名为SSTDBO）与其他优化算法进行比较，包括GODBO、QHDBO、DBO、KOA、NOA、WOA和HHO。结果表明，所提出的算法具有更强的鲁棒性和优化能力，算法性能得到了显著提升。最后，将所提出的算法应用于解决实际的机器人路径规划工程案例，以证明其在处理实际优化工程案例中的有效性，进一步验证了增强策略的有效性和增强算法在解决实际工程案例中的优越性是多么值得关注。
