增强型野马优化算法及其工程应用

Enhanced wild horse optimization algorithm and its engineering application

针对野马优化算法易陷入局部最优、收敛速度慢等缺点,提出增强型野马优化算法。首先,在种群初始化阶段,采用Sinusoidal映射,增加种群的多样性;其次,在阶段更新过程中,设计出非线性收敛性更强的自适应权重,调节全局搜索和局部优化能力;然后,在更新领导者位置阶段加入扰动因子,平衡局部和全局探索能力;进一步,利用自适应t分布变异,对个体位置进行扰动,提高算法跳出局部最优的能力。通过在CEC2021测试竞赛进行测试优化比较,验证算法的有效性和稳健性,并利用Wilcoxon秩和检验和MAE排名,验证算法的有效性。最后将算法应用到工程难题问题中,验证了其在工程优化问题上的适用性与优越性。实验结果表明,与其他智能算法相比,增强型野马优化算法具有更强的寻优能力和更快的收敛速度。

To overcome the weaknesses of wild horse optimization algorithm,such as easy to fall into local optima and slow convergence speed,this paper proposed an enhanced wild horse optimization algorithm.Firstly,in the population initialization stage,it utilized Sinusoidal mapping to increase the diversity of the population.Secondly,in the stage update process,it designed adaptive weights with stronger nonlinear convergence to adjust the abilities of global search and local optimization.Then,it introduced perturbation factors in the leader position update stage to balance local and global exploration capabilities.Furthermore,it utilized adaptive t-distribution mutation to perturb the individual positions and improve the algorithm’s ability to jump out of local optima.The effectiveness and robustness of the algorithm were validated by optimization comparisons in the CEC2021 competition test set,and the efficacy of the algorithm was verified through Wilcoxon rank sum test and MAE ranking.Finally,it applied the algorithm to two engineering problems,which verified the applicability and superiority of the algorithm for engineering optimization problems.The experimental results indicate that the enhanced wild horse optimization algorithm exhibits stronger optimization capabilities and faster convergence speed,which compared to other intelligent algorithms.