The performance of metaheuristic algorithms in solving high-dimensional, non-convex optimization problems is intricately linked to the balance between global exploration and local exploitation. Inspired by biomimetic principles of swarm intelligence, this study evaluates the Educational Competition Optimizer (ECO), a human learning-inspired metaheuristic, and addresses its vulnerability to rapid population homogenization and premature convergence in complex landscapes. To bridge the gap between rigid hierarchical competition and flexible biological cooperation, we propose the Comprehensive Learning-Enhanced Educational Competition Optimizer (CL-ECO), which introduces a dimension-wise multi-exemplar social learning mechanism to the ECO framework. Analogous to cooperative information sharing in animal swarms, CL-ECO reconstructs search trajectories by learning from different peers across decision variables, thereby promoting population diversity and adaptive exploration. Rigorous validation on the CEC 2017 benchmark suite demonstrates that CL-ECO achieves statistically superior convergence accuracy and robustness compared to seven state-of-the-art algorithms, securing the top Friedman rank (1.5862). Furthermore, the practical utility of CL-ECO is substantiated through a complex reservoir production optimization case study, where it outperforms the baseline algorithm in NPV maximization, proving its capability in managing complex, real-world engineering constraints.
Li et al. (Tue,) studied this question.