Coupling Ant Colony System with Local Search

In the last decades there has been a lot of interest in computational models and metaheuristics algorithms capable to solve combinatorial optimization problems. The recent trend is to define these algorithms taking inspiration by the observation of natural systems. In this thesis the Ant Colony System (ACS) is presented which has been inspired by the observation of real ant colonies. ACS is initially proposed to solve the symmetric and asymmetric travelling salesman problems where it is shown to be competitive with other metaheuristics. Although this is an interesting and promising result, it was immediately clear that ACS, as well as other metaheuristics, in many cases cannot compete with specialized local search methods. An interesting trend is therefore to couple metaheuristics with a local optimizer, giving birth to so-called hybrid methods. Along this line, the thesis investigates MACS-VRPTW (Multiple ACS for the Vehicle Routing Problem with Time Windows) and HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem (SOP). In the second part the thesis introduces some modifications of the original ACS algorithm. These modifications are able to speed up the method and to make it more competitive in case of large problem instances. The resulting framework, called Enhanced Ant Colony System is tested for the SOP. Finally the thesis presents the application of ACS to solve real-life vehicle routing problems where additional constraints and stochastic information are included.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/published