Improvement Whale Optimization Algorithm Based on Mixed Strategy

Abstract

As the original Whale Optimization Algorithm (WOA) has the drawback of falling into local extremes, it also fails to meet the expectations in terms of convergence effect. An improved whale optimization algorithm (HWOA) based on a hybrid strategy was proposed. First, the proposed algorithm is initialized based on the Zaslavskii chaotic map to obtain a population with better ergodicity; second, The elite search base strategy is used to improve the global optimization ability of the algorithm and increase the probability of jumping out of the local extreme value; Finally, by introducing an adaptive variable speed strategy, the search ability and development capabilities of the whale optimization algorithm are effectively coordinated while retaining the advantages of the algorithm. The improved whale optimization algorithm is tested against other algorithms on 10 benchmark functions. The final results demonstrate the effectiveness of the HWOA algorithm improvement strategy and outperforms other improvement algorithms in terms of effectiveness.