A multi-strategy ensemble social group optimization algorithm (ME-SGO) to improve the exploration for complex and composite landscapes through distance-based strategy adaption and success-based parameter adaption while incorporating linear population reduction is proposed. The proposed method is designed to achieve a better balance between exploration and exploitation with minimal tuning while overcoming the limitations of SGO. The proposed improved algorithm is tested and validated through CEC2019’s 100-digit competition, five engineering problems and compared against the standard version of SGO, four of its latest variants, five of the advanced state-of-the-art meta-heuristics, five modern meta-heuristics. Furthermore four complex problems on electric vehicle (EV) optimization namely, the optimal power flow problem with EV loading for IEEE 30 bus system (9 Cases) and IEEE 57 bus-system (9 cases) optimal reactive power dispatch with uncertainties in EV loading and intermittencies with PV and Wind energy systems for IEEE 30 bus system (25 scenarios), dynamic EV charging optimization (3 cases) and energy-efficient control of parallel hybrid electric vehicle (3 cases with 2 scenarios) covering the domains of power systems, energy and control optimization have been considered for validation through the proposed multi-strategy ensemble method and fifteen other state-of-the-art advanced and modern algorithms. The performance for the standard engineering problems and the EV optimization problems was excellent with good accuracy of the solutions and least standard deviation rates.

中文翻译：

---

基于动态平均一致性的直流微电网分布式定时二次控制


提出了一种多策略集成社会群体优化算法（ME-SGO），通过基于距离的策略适应和基于成功的参数适应，同时结合线性种群减少，改进对复杂和复合景观的探索。所提出的方法旨在以最小的调整实现探索和利用之间的更好平衡，同时克服 SGO 的局限性。所提出的改进算法通过 CEC2019 的 100 位数字竞赛、五个工程问题进行了测试和验证，并与 SGO 的标准版本、四个最新变体、五个先进的元启发式算法、五个现代元启发式。此外，关于电动汽车（EV）优化的四个复杂问题，即IEEE 30总线系统（9例）的EV负载最优潮流问题和IEEE 57总线系统（9例）电动汽车负载和不确定性下的最优无功功率调度问题IEEE 30总线系统的光伏和风能系统间歇性研究（25个场景）、动态电动汽车充电优化（3个案例）以及并联混合动力电动汽车的节能控制（3个案例2个场景），涵盖电力系统、能源领域和控制优化已被考虑通过所提出的多策略集成方法和其他十五种最先进的先进和现代算法进行验证。标准工程问题和 EV 优化问题的性能非常出色，解决方案精度高，标准偏差率最小。