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

Abstract

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

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

“…In the literature (Deb and Gao 2021), the author proposed CSO-ALO algorithm. The Ant Lion Optimization (ALO) algorithm's main principle is to replicate the way that ant lions pursue their prey in order to accomplish global optimization (Abualigah et al 2021).…”

“…The hybrid optimization technique adds more layers of optimization than typical optimization algorithms, which has benefits for accuracy. In literature (Deb and Gao 2021), it was effectively proved that the CSO-ALO hybrid algorithm considerably enhances the accuracy of the method when compared to the ALO and CSO algorithms in optimizing different load scheduling issues.…”

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

“…Bagherzadeh et al [253] proposed Salp Swarm Algorithm (SSA) to decrease the objective function of the problem to determine the optimal location and capacity of RES.SSA optimizer showed its preeminence with great attitude and correctness in problem-solving of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) [254]. S. Deb et al [255] proposed the Chicken Swarm Optimization (CSO) with Ant Lion Optimization (ALO) for efectively resolve the and efciently coping with the charger placement problem. Neuro-fuzzy is a term used in artificial intelligence to describe a method that combines fuzzy logic and artificial neural networks.…”

Multi‐objective quantum atom search optimization algorithm for electric vehicle charging station planning

“…Recently, hybrid variants of the chicken swarm optimization (CSO) algorithm have been developed for the ideal positioning of CSs. Particularly, Deb et al 14 devised a combination of CSO and teaching‐learning‐based optimization (TLBO) algorithm, while Deb and Gao 15 combined CSO with the ant lion optimization (ALO) algorithm to improve CSO performance. Both CSO‐TLBO and CSO‐ALO algorithms exhibit improved performances in comparison to their fundamental algorithms.…”