Improved YSGA Algorithm Combining Declining Strategy and Fuch Chaotic Mechanism

Abstract

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.

Keywords

• intelligent optimization algorithm (ioa)
• yellow saddle goatfish algorithm (ysga)
• step size factor reduction strategy
• chaos enhancement mechanism
• fuch mapping