Towards a Better Understanding of Genetic Operators for Ordering Optimization: Application to the Capacitated Vehicle Routing Problem

S. Ben Hamida, R. Gorsane, K. Mestiri

2020

Abstract

Genetic Algorithms (GA) have long been used for ordering optimization problems with some considerable efforts to improve their exploration and exploitation abilities. A great number of GA implementations have been proposed varying from GAs applying simple or advanced variation operators to hybrid GAs combined with different heuristics. In this work, we propose a short review of genetic operators for ordering optimization with a classification according to the information used in the reproduction step. Crossover operators could be position (”blind”) operators or heuristic operators. Mutation operators could be applied randomly or using local optimization. After studying the contribution of each class on solving two benchmark instances of the Capacitated Vehicle Routing Problem (CVRP), we explain how to combine the variation operators to allow simultaneously a better exploration of the search space with higher exploitation. We then propose the random and the balanced hybridization of the operators’ classes. The hybridization strategies are applied to solve 24 CVRP benchmark instances. Results are analyzed and compared to demonstrate the role of each class of operators in the evolution process.

Paper Citation

in Harvard Style

Ben Hamida S., Gorsane R. and Mestiri K. (2020). Towards a Better Understanding of Genetic Operators for Ordering Optimization: Application to the Capacitated Vehicle Routing Problem.In Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-443-5, pages 461-469. DOI: 10.5220/0009832704610469

in Bibtex Style

@conference{icsoft20,
author={S. Ben Hamida and R. Gorsane and K. Mestiri},
title={Towards a Better Understanding of Genetic Operators for Ordering Optimization: Application to the Capacitated Vehicle Routing Problem},
booktitle={Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2020},
pages={461-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009832704610469},
isbn={978-989-758-443-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Towards a Better Understanding of Genetic Operators for Ordering Optimization: Application to the Capacitated Vehicle Routing Problem
SN - 978-989-758-443-5
AU - Ben Hamida S.
AU - Gorsane R.
AU - Mestiri K.
PY - 2020
SP - 461
EP - 469
DO - 10.5220/0009832704610469