A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem
A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electr...
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doaj-35e989fd607e4db093bfef96cd65aec82021-03-30T01:35:44ZengIEEEIEEE Access2169-35362020-01-018925739259010.1109/ACCESS.2020.29942989091834A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement ProblemSanchari Deb0https://orcid.org/0000-0002-1032-1081Kari Tammi1https://orcid.org/0000-0001-9376-2386Xiao-Zhi Gao2https://orcid.org/0000-0002-0078-5675Karuna Kalita3https://orcid.org/0000-0002-9961-6117Pinakeswar Mahanta4https://orcid.org/0000-0003-0190-6608Centre for Energy, Indian Institute of Technology, Guwahati, IndiaDepartment of Mechanical Engineering, Aalto University, Espoo, FinlandSchool of Computing, University of Eastern Finland, Kuopio, FinlandDepartment of Mechanical Engineering, Indian Institute of Technology, Guwahati, IndiaDepartment of Mechanical Engineering, Indian Institute of Technology, Guwahati, IndiaA new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.https://ieeexplore.ieee.org/document/9091834/Accessibility indexcharging stationchicken swarm optimizationteaching learning optimizationcostelectric vehicle |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanchari Deb Kari Tammi Xiao-Zhi Gao Karuna Kalita Pinakeswar Mahanta |
spellingShingle |
Sanchari Deb Kari Tammi Xiao-Zhi Gao Karuna Kalita Pinakeswar Mahanta A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem IEEE Access Accessibility index charging station chicken swarm optimization teaching learning optimization cost electric vehicle |
author_facet |
Sanchari Deb Kari Tammi Xiao-Zhi Gao Karuna Kalita Pinakeswar Mahanta |
author_sort |
Sanchari Deb |
title |
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem |
title_short |
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem |
title_full |
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem |
title_fullStr |
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem |
title_full_unstemmed |
A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem |
title_sort |
hybrid multi-objective chicken swarm optimization and teaching learning based algorithm for charging station placement problem |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem. |
topic |
Accessibility index charging station chicken swarm optimization teaching learning optimization cost electric vehicle |
url |
https://ieeexplore.ieee.org/document/9091834/ |
work_keys_str_mv |
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