融合递减策略与Fuch混沌机制的改进YSGA算法

Improved YSGA Algorithm Combining Declining Strategy and Fuch Chaotic Mechanism

为增强绯鲵鲣算法搜索的覆盖性及寻优的精准性以优化全局探索能力和局部开采能力,提出一种融合步长因子递减策略与混沌局部增强机制的改进绯鲵鲣优化算法(IYSGA)。首先,该改进算法在标准YSGA算法基础上,设计了一种动态的步长因子递变模式以实现绯鲵鲣算法高效全面的搜索,此策略有利于提高算法的搜索效率并扩大寻优范围;其次,混沌搜索机制则是借鉴Fuch映射理论优越的混沌特性与较好的局部收敛性能而构造的一种当前最优解的局部再开采方式,以完成对YSGA算法的局部搜索性能的改善。该耦合方法对YSGA的改进,有利于实现IYSGA算法全局探索与局部搜索能力间的多轮动态迭代平衡。最后,通过数值实验验证了IYSGA算法优越的并行迭代寻优性能与稳健性。

In order to enhance the search coverage and optimization accuracy of the Goatfish algorithm to optimize the global exploration ability and local mining ability,an improved Goatfish optimization algorithm IYSGA(improved yellow saddle goatfish algorithm)is proposed combining a step size factor reduction strategy and a chaotic local enhancement mechanism.Firstly,the improved algorithm is based on the standard YSGA algorithm,and designs a dynamic step-factor variable mode to achieve efficient and comprehensive search for the goatfish algorithm.This strategy is conducive to improving the search efficiency of the algorithm and expanding the scope of optimization.Secondly,the chaos search mechanism is a local re-mining method of constructing the current optimal solution based on the superior chaotic characteristics of Fuch mapping theory and better local convergence performance to complete the improvement of the local search performance of the YSGA algorithm.The improvement of YSGA by this coupling method is beneficial to realize the multi-round dynamic iterative balance between global exploration and local search capability of IYSGA algorithm.Finally,numerical experiments verify the superior parallel iteration optimization performance and robustness of the IYSGA algorithm.