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...
| Main Authors: | Agany Manyiel, J.M., Kwang Hooi, Y., Zakaria, M.N.B. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30344 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
|
| 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/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|