Investigation of a Multi-Strategy Ensemble Social Group Optimization Algorithm for the Optimization of Energy Management in Electric Vehicles

Investigation of a Multi-Strategy Ensemble Social Group Optimization Algorithm for the Optimization of Energy Management in Electric Vehicles

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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 2012 honda civic si coilovers 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 x515ea-bh55-cb 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.