An Improved Whale Optimization Algorithm with Random Evolution and Special Reinforcement Dual-Operation Strategy Collaboration

In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration. Firstly, different evolutionary strategies are integrated into different dimensions of the algorithm structure to improve the convergence accuracy and the randomization operation of the random Gaussian distribution is used to increase the diversity of the population. Secondly, special reinforcements are made to the process involving whales searching for prey to enhance their exclusive exploration or exploitation capabilities, and a new skip step factor is proposed to enhance the optimizer's ability to escape the local optimum. Finally, an adaptive weight factor is added to improve the stability of the algorithm and maintain a balance between exploration and exploitation. The effectiveness and feasibility of the proposed REWOA are verified with the benchmark functions and different experiments related to the identification of the Hammerstein model.

Automated summary

An improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration to address the slow convergence speed, difficulty escaping from the local optimum, and stability issues associated with the basic WOA algorithm. The REWOA algorithm integrates different evolutionary strategies, increases population diversity, strengthens whales' exploration and exploitation capabilities, and introduces a new skip step factor to enhance optimizer's ability to escape the local optimum. Additionally, an adaptive weight factor is added to improve algorithm stability and maintain a balance between exploration and exploitation.