TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
SP  - 213
AV  - none
ID  - scholars14687
A1  - Agany Manyiel, J.M.
A1  - Kwang Hooi, Y.
A1  - Zakaria, M.N.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112464766&doi=10.1109%2fICCOINS49721.2021.9497136&partnerID=40&md5=54bef794176e6bb186b8db8dd38c147d
N1  - 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
EP  - 219
SN  - 9781728171517
N2  - 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.
KW  - Routing algorithms; Stochastic systems; Vehicle routing; Vehicles
KW  -  Approximate solution; Meta-heuristic methods; Multi-objective genetic algorithm; Multi-population genetic algorithm; Multiple populations; Pre-mature convergences; Single objective; Vehicle Routing Problems
KW  -  Genetic algorithms
TI  - Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems
Y1  - 2021///
ER  -