An Enhanced Polar Lights Optimization Algorithm with Symmetry Mechanisms for Global Optimization and Its Application to Wind Power Forecasting

Optimization problems in high-dimensional, nonlinear, and multimodal scenarios remain challenging for conventional methods. Although metaheuristic algorithms have shown strong adaptability, many still suffer from premature convergence, inefficient boundary handling, insufficient population diversity, and a lack of effective symmetry exploitation in the search space. To address these limitations, this paper proposes an Enhanced Polar Lights Optimization (EPLO) algorithm, which extends the recently developed Polar Lights Optimization (PLO) method by incorporating symmetry-aware optimization mechanisms. EPLO integrates three complementary strategies—an elite-guided strategy, a global-best-informed boundary control strategy, and a hybrid crossover strategy—forming a unified “Guidance–Constraint–Innovation” optimization mechanism that leverages symmetry to balance exploration and exploitation. Specifically, elite guidance improves search directionality and convergence speed, symmetry-informed boundary control enhances solution validity and stability, and hybrid crossover preserves population diversity by maintaining symmetric and diverse solution distributions. The performance of EPLO is comprehensively evaluated on the CEC2017 and CEC2022 benchmark suites and compared with nine representative metaheuristic algorithms. Statistical analyses using Wilcoxon rank-sum and Friedman tests confirm the superior convergence speed, robustness, and solution quality of EPLO. Furthermore, to demonstrate its practical applicability, EPLO is combined with a BP neural network and applied to a wind power forecasting task, where symmetry-enhanced optimization contributes to improved predictive accuracy. The results validate EPLO as an effective and robust optimization algorithm with strong potential for both benchmark problems and real-world applications.