A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

Abstract Transportation electrification is known to be a viable alternative deal with the alarming issues of global warming, air pollution, and energy crisis. Public acceptance Electric Vehicles (EVs) requires availability charging infrastructure. However, optimal placement chargers indeed complex problem multiple design variables, objective functions, constraints. Chargers must placed EV drivers’ convenience security power distribution network being taken into account. The solutions such an emerging optimization are mostly based on metaheuristics. This work proposes novel metaheuristic considering hybridization Chicken Swarm Optimization (CSO) Ant Lion (ALO) for effectively efficiently coping charger problem. amalgamation CSO ALO can enhance performance ALO, thereby preventing it from getting stuck in local optima. Our hybrid algorithm has strengths both which tested standard benchmark functions as well above Simulation results demonstrate that performs moderately better than counterpart methods.