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.

电风暴优化（Electrical Storm Optimization, ESO）算法受电风暴动态特性启发，是一种新型基于种群的元启发式算法（metaheuristic），采用三个动态调整的参数，即场电阻（field resistance）、场强度（intensity）与场传导率（conductivity）。场电阻评估解在搜索空间中的分布，反映策略多样性；场强度平衡对新区域的探索与对潜在优质区域的利用；场传导率调整搜索过程的适应性，提升算法逃离局部最优（local optima）的能力。这些调整使ESO能实时适应各类优化场景，引导搜索向潜在最优解方向推进。针对包括IEEE CEC SOBC 2022套件在内的65个基准问题（benchmark problems），以及20种知名元启发式算法，ESO的性能得到了严格测试。结果表明，ESO具有优越性能，尤其在需要探索与利用之间精细平衡的任务中表现突出。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，凸显了其精度、稳定性、灵活性与效率。