An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP

In many practical problems, several conflicting criteria exist for evaluating solutions. In recent years, strong research efforts have been made to develop efficient algorithmic techniques for tackling such multi-objective optimization problems. Many of these algorithms are extensions of well-known metaheuristics. In particular, over the last few years, several extensions of ant colony optimization (ACO) algorithms have been proposed for solving multi-objective problems. These extensions often propose multiple answers to algorithmic design questions arising in a multi-objective ACO approach. However, the benefits of each one of these answers are rarely examined against the alternative approaches. This article reports results of an empirical research effort aimed at analyzing the components of ACO algorithms for tackling multi-objective combinatorial problems. The results presented here focus on the bi-objective travelling salesman problem. The main goal is to study the effect of algorithmic components and their possible interactions on the performance of a multi-objective ACO algorithm. Examples of design choices are the use of local search, the use of one versus several pheromone matrices, and the use of one or several ant colonies. The analysis of the results is carried out by means of an exploratory graphical analysis tool based on the attainment function methodology. This tool allows to identify regions of the objective space where there is a strong difference between two alternative strategies.