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Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms

by Padmabati Chand, J. R. Mohanty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 26
Year of Publication: 2013
Authors: Padmabati Chand, J. R. Mohanty
10.5120/12137-8421

Padmabati Chand, J. R. Mohanty . Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms. International Journal of Computer Applications. 69, 26 ( May 2013), 34-41. DOI=10.5120/12137-8421

@article{ 10.5120/12137-8421,
author = { Padmabati Chand, J. R. Mohanty },
title = { Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 26 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number26/12137-8421/ },
doi = { 10.5120/12137-8421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:31:23.061576+05:30
%A Padmabati Chand
%A J. R. Mohanty
%T Solving Vehicle Routing Problem with Proposed Non- Dominated Sorting Genetic Algorithm and Comparison with Classical Evolutionary Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 26
%P 34-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multi objective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm for big problem gives less efficient results, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in giving good results but, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which gives better efficient results. The improved algorithm is applied to the transportation problem: vehicle routing problem (VRP). Results of comparative tests are presented showing that the improved algorithm performs well on large populations.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm (GA) Multi Objective Genetic Algorithm (MOGA) Weighted based Genetic Algorithm (WBGA) Non Dominated Sorted Genetic Algorithm (NSGA) Vehicle Routing Problem (VRP)

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