eprintid: 14687
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/46/87
datestamp: 2023-11-10 03:29:16
lastmod: 2023-11-10 03:29:16
status_changed: 2023-11-10 01:57:33
type: conference_item
metadata_visibility: show
creators_name: Agany Manyiel, J.M.
creators_name: Kwang Hooi, Y.
creators_name: Zakaria, M.N.B.
title: Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems
ispublished: pub
keywords: Routing algorithms; Stochastic systems; Vehicle routing; Vehicles, Approximate solution; Meta-heuristic methods; Multi-objective genetic algorithm; Multi-population genetic algorithm; Multiple populations; Pre-mature convergences; Single objective; Vehicle Routing Problems, Genetic algorithms
note: cited By 0; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762
abstract: Genetic algorithm (GA) is a metaheuristic method that has been widely adopted for solving the Rich Vehicle Routing Problems (RVRP) due to its ability to find quality approximate solutions, even for large-scale instances of the problem, in a reasonable time. However, GA is stochastic in nature and does not guarantee a good solution all the time, a problem primarily due to premature convergence. In this paper we present Multi-population Genetic Algorithm for Rich Vehicle Routing Problems (MPGA-RVRP) to provide diversity and delay premature convergence in GA by making use of multiple populations that each evolves independently optimizing a single objective while sharing potential solutions. MPGA-RVRP is applied in RVRP with three objectives:- total route distance, total route duration and total route cost. Results from the experiments show that MPGA-RVRP performs considerably better compared to benchmark, multi-objective Genetic Algorithm (MOGA). © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112464766&doi=10.1109%2fICCOINS49721.2021.9497136&partnerID=40&md5=54bef794176e6bb186b8db8dd38c147d
id_number: 10.1109/ICCOINS49721.2021.9497136
full_text_status: none
publication: Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021
pagerange: 213-219
refereed: TRUE
isbn: 9781728171517
citation:   Agany Manyiel, J.M. and Kwang Hooi, Y. and Zakaria, M.N.B.  (2021) Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems.  In: UNSPECIFIED.