Raw results of the ESO algorithm

The Electrical Storm Optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, intensity, and conductivity. Field resistance assesses the spread of solutions within the search space, reflecting strategy diversity. Field intensity balances the exploration of new territories and the exploitation of promising areas. Field conductivity adjusts the adaptability of the search process, enhancing the algorithm's ability to escape local optima. These adjustments enable ESO to adapt in real-time to various optimization scenarios, steering the search toward potential optima. The ESO's performance was rigorously tested against 65 benchmark problems including the IEEE CEC SOBC 2022 suite and 20 well-known metaheuristics. Results demonstrated ESO's superior performance, particularly in tasks requiring a nuanced balance between exploration and exploitation. Its efficacy is further validated through successful applications in four engineering domains, highlighting its precision, stability, flexibility, and efficiency.

电气风暴优化（ESO）算法，灵感源于电气风暴的动态特性，是一种创新的基于种群的元启发式算法，该算法采用三个动态调整的参数：场电阻、强度和电导率。场电阻评估解决方案在搜索空间中的扩散情况，反映策略多样性。场强度平衡对新领域的探索和对有希望区域的开发。场电导率调整搜索过程的适应性，增强算法逃离局部最优解的能力。这些调整使得ESO能够实时适应各种优化场景，引导搜索向潜在最优解方向。ESO的性能经过对包括IEEE CEC SOBC 2022套件和20种知名元启发式算法在内的65个基准问题的严格测试。结果表明，ESO在需要微妙平衡探索和开发的任务中表现出卓越的性能。其有效性通过在四个工程领域的成功应用得到进一步验证，凸显了其精确性、稳定性、灵活性和效率。