CFOA(Catch Fish Optimization Algorithm) usually includes two update stages, the exploration stage and the exploitation stage. However, this algorithm is still prone to getting stuck in local optima and has a relatively low convergence rate. To address these issues, the ICFOA ( Improved Catch Fish Optimization Algorithm) is proposed based on personalized fishing strategies. Firstly, an adaptive Gaussian perturbation is introduced in the exploration stage to enhance the global search ability and efficiency while avoiding local optima. Secondly, based on personalized fishing strategies, the positions of fishermen are updated by randomly selecting either the “ bare-handed fishing “factor or the “ using a fishing net ”factor to accelerate the convergence. The CEC2020 test suite is used to conduct comparative experiments to evaluate the performance differences between ICFOA and other excellent meta-heuristic algorithms, and the Wilcoxon rank sum test is used to verify the validity of the statistical results. Finally, the maximum power point tracking in photovoltaic power plants, ICFOA effectively reduces the power tracking deviation, demonstrating its effectiveness in solving practical problems. Experimental results indicate that ICFOA possesses greater competitiveness compared to the original CFOA.

catch fish optimization algorithm, meta-heuristic algorithms, swarm intelligence, personalized fishing strategy