A Multi-Strategy Improved Catch Fish Optimization Algorithm for Microgrid Scheduling Optimization and Real-World Engineering Applications

Complex engineering optimization problems are typically characterized by high dimensionality, multimodality, and strong constraints, posing significant challenges to traditional swarm intelligence algorithms in terms of convergence speed, solution accuracy, and robustness. The Catch Fish Optimization Algorithm (CFOA), a recently proposed swarm-based metaheuristic, exhibits promising global search capability; however, it still suffers from deficiencies in search direction stability, elite solution utilization, and exploitation performance in the later stages of optimization. To address these limitations, this paper proposes an Improved Catch Fish Optimization Algorithm, named Elite-Driven Reinforced Catch Fish Optimization Algorithm (EDR-CFOA). On the basis of the original CFOA framework, EDR-CFOA integrates three complementary elite-based enhancement strategies: an elite-enhanced search strategy, an elite differential evolution strategy, and an elite random local search strategy. Through a multi-level elite-guided mechanism, these strategies collaboratively improve the reliability of search directions, strengthen solution-space recombination, and enhance fine-grained exploitation of high-quality solutions, thereby significantly improving the overall optimization performance of the algorithm. The proposed EDR-CFOA is systematically evaluated on the CEC2020 and CEC2022 benchmark test suites under 10-dimensional and 20-dimensional settings and is compared with eight classical and recently developed high-performance metaheuristic algorithms. The Friedman mean ranking results demonstrate that EDR-CFOA achieves the lowest average rank in all four test scenarios (CEC2020: 1.30 for 10D and 2.20 for 20D; CEC2022: 1.17 for 10D and 1.08 for 20D), consistently ranking first overall and significantly outperforming the competing algorithms. Furthermore, Wilcoxon rank-sum tests confirm that EDR-CFOA exhibits statistically significant superiority on the majority of benchmark functions. In addition, EDR-CFOA is applied to the economic optimal scheduling problem of a grid-connected microgrid and several typical constrained engineering design problems, where experimental results verify its feasibility, robustness, and practical engineering applicability. Comprehensive numerical experiments and real-world engineering case studies indicate that EDR-CFOA is a highly effective swarm intelligence algorithm featuring high solution accuracy, strong stability, and excellent generalization capability, making it well suited for complex engineering optimization problems.