Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems
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 do...
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Institute of Electrical and Electronics Engineers Inc.
2021
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my.utp.eprints.303442022-03-25T06:44:00Z Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems Agany Manyiel, J.M. Kwang Hooi, Y. Zakaria, M.N.B. 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. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112464766&doi=10.1109%2fICCOINS49721.2021.9497136&partnerID=40&md5=54bef794176e6bb186b8db8dd38c147d 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. http://eprints.utp.edu.my/30344/ |
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Universiti Teknologi Petronas |
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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. |
| format |
Conference or Workshop Item |
| author |
Agany Manyiel, J.M. Kwang Hooi, Y. Zakaria, M.N.B. |
| spellingShingle |
Agany Manyiel, J.M. Kwang Hooi, Y. Zakaria, M.N.B. Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| author_facet |
Agany Manyiel, J.M. Kwang Hooi, Y. Zakaria, M.N.B. |
| author_sort |
Agany Manyiel, J.M. |
| title |
Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| title_short |
Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| title_full |
Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| title_fullStr |
Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| title_full_unstemmed |
Multi-Population Genetic Algorithm for Rich Vehicle Routing Problems |
| title_sort |
multi-population genetic algorithm for rich vehicle routing problems |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2021 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112464766&doi=10.1109%2fICCOINS49721.2021.9497136&partnerID=40&md5=54bef794176e6bb186b8db8dd38c147d http://eprints.utp.edu.my/30344/ |
| _version_ |
1738657094346735616 |
| score |
13.250246 |