Nature-inspired approach: An enhanced whale optimization algorithm for global optimization

Abstract

The whale optimization algorithm is based on the bubble-net attacking behavior of humpback whales and simulates encircling prey, bubble-net attacking and searching for prey to obtain the global optimal solution. However, the basic whale optimization algorithm has the disadvantage of search stagnation, easily falls into a local optimum, has slow convergence speed and has low calculation accuracy. The Lévy flight strategy is beneficial for expanding the search range and prevents the algorithm from falling into a local optimum, which enhances the global search ability. The ranking-based mutation operator can increase the selection probability and accelerate the convergence speed to enhance the local search ability. To overcome these shortcomings and avoid premature convergence, the Lévy flight strategy and the ranking-based mutation operator are added to the whale optimization algorithm. In this paper, an enhanced whale optimization algorithm is proposed, which realizes complementary advantages to balance exploration and exploitation. Eighteen benchmark test functions and five structural engineering design problems are used to verify the robustness and overall optimization performance of the enhanced whale optimization algorithm. The experimental results show that the enhanced whale optimization algorithm is an effective and feasible method that has a fast convergence speed, high calculation accuracy, strong robustness and stability.