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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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: | 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/ |
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| Summary: | 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. |
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