Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Umair Farooq Siddiqi; Yoichi Shiraishi; Sadiq M. Sait

doi:10.1109/ISDA.2011.6121687

Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Umair Farooq Siddiqi^*
, Yoichi Shiraishi
, Sadiq M. Sait

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

46 Scopus citations

Abstract

Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H-MCOP.

Original language	English
Title of host publication	Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Pages	391-396
Number of pages	6
DOIs	https://doi.org/10.1109/ISDA.2011.6121687
State	Published - 2011

Publication series

Name	International Conference on Intelligent Systems Design and Applications, ISDA
ISSN (Print)	2164-7143
ISSN (Electronic)	2164-7151

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Electric Vehicles (EVs)
Multi Constrained Optimal Path
Route Optimization
Simulated Evolution (SimE)

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Signal Processing
Control and Systems Engineering

Access to Document

10.1109/ISDA.2011.6121687

Cite this

Siddiqi, U. F., Shiraishi, Y., & Sait, S. M. (2011). Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO). In Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11 (pp. 391-396). Article 6121687 (International Conference on Intelligent Systems Design and Applications, ISDA). https://doi.org/10.1109/ISDA.2011.6121687

Siddiqi, Umair Farooq ; Shiraishi, Yoichi ; Sait, Sadiq M. / Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO). Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11. 2011. pp. 391-396 (International Conference on Intelligent Systems Design and Applications, ISDA).

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title = "Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)",

abstract = "Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375\% more test instances than the GA and H-MCOP.",

keywords = "Electric Vehicles (EVs), Multi Constrained Optimal Path, Route Optimization, Simulated Evolution (SimE)",

author = "Siddiqi, \{Umair Farooq\} and Yoichi Shiraishi and Sait, \{Sadiq M.\}",

year = "2011",

doi = "10.1109/ISDA.2011.6121687",

language = "English",

isbn = "9781457716751",

series = "International Conference on Intelligent Systems Design and Applications, ISDA",

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Siddiqi, UF, Shiraishi, Y & Sait, SM 2011, Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO). in Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11., 6121687, International Conference on Intelligent Systems Design and Applications, ISDA, pp. 391-396. https://doi.org/10.1109/ISDA.2011.6121687

Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO). / Siddiqi, Umair Farooq; Shiraishi, Yoichi; Sait, Sadiq M.
Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11. 2011. p. 391-396 6121687 (International Conference on Intelligent Systems Design and Applications, ISDA).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

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AU - Shiraishi, Yoichi

AU - Sait, Sadiq M.

PY - 2011

Y1 - 2011

N2 - Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H-MCOP.

AB - Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H-MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H-MCOP.

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BT - Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11

ER -

Siddiqi UF, Shiraishi Y, Sait SM. Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO). In Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11. 2011. p. 391-396. 6121687. (International Conference on Intelligent Systems Design and Applications, ISDA). doi: 10.1109/ISDA.2011.6121687

Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Abstract

Publication series

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this