Sensitive Ants in Solving the Generalized Vehicle Routing Problem

TL;DR

This paper explores the use of sensitive ant colony algorithms with variable pheromone sensitivity to improve solutions for the generalized vehicle routing problem, highlighting the benefits of diversity and intensified search.

Contribution

It introduces a hybrid ant-based model with heterogeneous pheromone sensitivity levels for solving complex vehicle routing problems.

Findings

Sensitive ant models enhance exploration and solution quality.

Numerical results show improved performance over traditional methods.

Hybrid models offer promising benefits for combinatorial optimization.

Abstract

The idea of sensitivity in ant colony systems has been exploited in hybrid ant-based models with promising results for many combinatorial optimization problems. Heterogeneity is induced in the ant population by endowing individual ants with a certain level of sensitivity to the pheromone trail. The variable pheromone sensitivity within the same population of ants can potentially intensify the search while in the same time inducing diversity for the exploration of the environment. The performance of sensitive ant models is investigated for solving the generalized vehicle routing problem. Numerical results and comparisons are discussed and analysed with a focus on emphasizing any particular aspects and potential benefits related to hybrid ant-based models.