Multi-strategy Adaptive Whale Optimization Algorithm

The Whale optimization Algorithm (WOA) is a metaheuristic algorithm proposed based on the simulation of the hunting behavior of whales. It has advantages such as simplicity and few adjustable parameters. However, WOA suffers from insufficient exploration and exploitation abilities in the later stage of iteration, resulting in low solution accuracy and a tendency to get trapped in local optima. In response to the shortcomings of WOA, including slow convergence speed, low accuracy, inadequate global exploration ability, and a tendency to get trapped in local optima, an improved Whale optimization Algorithm (MSIWOA) is proposed. Firstly, a nonlinear decreasing mechanism for iteration number is designed to balance the global search and local exploitation abilities. Additionally, a Cauchy mutation mechanism is introduced to improve the problem of low convergence accuracy and avoid getting trapped in local optima. The effectiveness of the MSIWOA algorithm is tested on 19 widely used test functions, and the results demonstrate that the proposed algorithm has better performance than traditional WOA and other improved algorithms, proving the effectiveness of the MSIWOA algorithm.