Multi-Strategy Improved Whale Optimization Algorithm and Its Engineering Applications

Abstract

The Whale Optimization Algorithm (WOA) is recognized for its simplicity, few control parameters, and effective local optima avoidance. However, it struggles with global search efficiency and slow convergence. This paper introduces the Improved WOA (ImWOA) to overcome these challenges. Initially, ImWOA utilizes a dynamic elastic boundary optimization strategy, which leverages boundary information and the current optimal position to guide solutions that exceed the boundaries back within permissible limits, gradually converging towards the optimal solution. Subsequently, ImWOA integrates an advanced random searching strategy that equilibrates global and local searches by focusing on the current optimal location and the mean position of all individuals. Lastly, a combined mutation mechanism is employed to enhance population diversity, prevent the algorithm from stagnating in local optima, and consequently augment its overall search capability. Performance evaluations on CEC2017 benchmark functions show ImWOA outperforming five metaheuristic algorithms and three WOA variants in optimization accuracy, stability, and convergence speed. ImWOA excelled in 25 out of 29 test functions in 30D and 26 out of 29 in 100D scenarios. Furthermore, its efficacy in addressing complex challenges is corroborated by real-world applications in reducer design, vehicle side impact design, and welded beam design, highlighting its potential utility across various engineering domains.