MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems.

Area of Science:

• Computational Intelligence
• Meta-heuristic Optimization
• Algorithm Design

Background:

• The Catch Fish Optimization Algorithm (CFOA) is a novel meta-heuristic algorithm inspired by human behaviors, showing promise in test functions and clustering.
• However, CFOA exhibits limitations in convergence accuracy and escaping local optima, necessitating performance enhancements.

Purpose of the Study:

• To introduce a Multi-Strategy Improved Catch Fish Optimization Algorithm (MICFOA) to address the shortcomings of the original CFOA.
• To enhance global search capabilities, improve convergence accuracy, and increase robustness in numerical optimization problems.

Main Methods:

• Incorporated a Lévy-based differential independent search strategy in the exploration phase for improved global search.
• Implemented a weight-balanced selection mechanism in the exploitation phase to maintain diversity and escape local optima.
• Introduced a fishermen position replacement strategy to bolster algorithmic robustness.

Main Results:

• MICFOA demonstrated significant dominance in numerical optimization across CEC 2017 and CEC 2022 test functions.
• Comprehensive comparisons showed MICFOA outperformed CFOA and other state-of-the-art algorithms like LSHADE and JADE.

Conclusions:

• The proposed MICFOA effectively enhances the performance of the original CFOA.
• MICFOA offers a robust and accurate solution for numerical optimization problems, surpassing existing methods.