Whale Optimization Algorithm Improved Effectiveness Analysis Based on Compound Chaos Optimization Strategy and Dynamic Optimization Parameters

Abstract

Basing on the fact that the basic whale optimization algorithm has the defects including low convergence precision and it is easy to fall into the local optimal solution when solving the objective function whose optimal solution is not near the origin. A new whale optimization algorithm (Dio-WOA) based on compound chaos optimization strategy and dynamic improved parameters is proposed. In the algorithm, the chaos optimization strategy set is first introduced for multiple solution links. At the same time, the convergence factor of WOA a is improved and the inertia weight w is introduced, so the algorithm can slow down the convergence speed in the early stage for each generation to fully explore the overall algorithm and accelerate in the later iteration to improve the accuracy of the algorithm. At the same time, chaos strategy concentrates on the chaos optimization strategy for the optimal solution can help the algorithm effectively choose the solution out of the local optimal solution. This paper verifies the comprehensive performance of the Dio-WOA algorithm and the effectiveness of various improvement measures. Several undetermined dimension single-peak and multi-peak test functions are introduced to verify the performance of the overall algorithm and the local improvement algorithm. The results show that the single improvement measures can effectively improve the performance of the algorithm and each measure has different performance directions. The comprehensive performance of Dio-WOA is better than that of single improvement measures, which proves the effective compatibility between the improvement measures.