Multi constrained route optimization for electric vehicles using SimE

Umair F. Siddiqi; Yoichi Shiraishi; Sadiq M. Sait

doi:10.1109/SoCPaR.2011.6089273

Multi constrained route optimization for electric vehicles using SimE

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

^*Corresponding author for this work

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

14 Scopus citations

Abstract

Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5N (N is the population size and N ≥ 2 and generally N = 20) times lesser memory than the GA.

Original language	English
Title of host publication	Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011
Pages	376-383
Number of pages	8
DOIs	https://doi.org/10.1109/SoCPaR.2011.6089273
State	Published - 2011

Publication series

Name	Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Multi Constrained Optimal Path
Route Optimization
Simulated Evolution (SimE)

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Software

Access to Document

10.1109/SoCPaR.2011.6089273

Cite this

Siddiqi, U. F., Shiraishi, Y., & Sait, S. M. (2011). Multi constrained route optimization for electric vehicles using SimE. In Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011 (pp. 376-383). Article 6089273 (Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011). https://doi.org/10.1109/SoCPaR.2011.6089273

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title = "Multi constrained route optimization for electric vehicles using SimE",

abstract = "Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5N (N is the population size and N ≥ 2 and generally N = 20) times lesser memory than the GA.",

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

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

year = "2011",

doi = "10.1109/SoCPaR.2011.6089273",

language = "English",

isbn = "9781457711947",

series = "Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011",

pages = "376--383",

booktitle = "Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011",

}

Siddiqi, UF, Shiraishi, Y & Sait, SM 2011, Multi constrained route optimization for electric vehicles using SimE. in Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011., 6089273, Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011, pp. 376-383. https://doi.org/10.1109/SoCPaR.2011.6089273

Multi constrained route optimization for electric vehicles using SimE. / Siddiqi, Umair F.; Shiraishi, Yoichi; Sait, Sadiq M.
Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011. 2011. p. 376-383 6089273 (Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011).

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

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AU - Siddiqi, Umair F.

AU - Shiraishi, Yoichi

AU - Sait, Sadiq M.

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N2 - Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5N (N is the population size and N ≥ 2 and generally N = 20) times lesser memory than the GA.

AB - Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5N (N is the population size and N ≥ 2 and generally N = 20) times lesser memory than the GA.

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Siddiqi UF, Shiraishi Y, Sait SM. Multi constrained route optimization for electric vehicles using SimE. In Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011. 2011. p. 376-383. 6089273. (Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011). doi: 10.1109/SoCPaR.2011.6089273

Multi constrained route optimization for electric vehicles using SimE

Abstract

Publication series

UN SDGs

Keywords

ASJC Scopus subject areas

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