Optimization algorithm and engineering applications for fishing based on hybrid strategy improvement

To address the issues of the catch fish optimization algorithm (CFOA), such as its tendency to fall into local optima and low population diversity in the later iterations, a multi-strategy integrated improved CFOA is proposed. Firstly, the population is initialized through the reverse learning strategy to enhance the quality of the initial population. Secondly, the group leader convergence adaptive teaming strategy is adopted to strengthen the learning of advantageous experiences. Finally, the Lévy flight spiral search strategy is introduced to improve the algorithm's ability to escape from local optima in the collective capture stage. The improved algorithm is compared with seven other algorithms, including grey wolf optimization (GWO) algorithm, sparrow search algorithm optimization (SSAO), whale optimization algorithm (WOA), and sine cosine algorithm (SCA), on 15 benchmark test functions. The experimental results demonstrate that the improved algorithm exhibits better performance in terms of solution accuracy and convergence speed. Furthermore, simulation experiments on three engineering design optimization problems further verify the superiority of the improved algorithm in handling engineering optimization problems.