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

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 in...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 8; no. 4; pp. 2791 - 2808
Main Authors: Deb, Sanchari, Gao, Xiao-Zhi
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.08.2022
Springer Nature B.V
Subjects:
Algorithms
Charging
Complexity
Computational Intelligence
Data Structures and Information Theory
Electric power distribution
Electric vehicles
Electrification
Electronic equipment
Engineering
Heuristic methods
Optimization
Original Article
Placement
Metaheuristics
Chicken swarm optimization
Swarm intelligence
Electric vehicle
Charger
Optimization
Ant lion optimization
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary: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.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-021-00510-x