Future Generation Computer Systems
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Bi-Criterion Optimization with Multi Colony Ant Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Ant Colony Optimization for Multi-Objective Optimization Problems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Speed-up techniques for solving large-scale biobjective TSP
Computers and Operations Research
Population-based ant colony optimisation for multi-objective function optimisation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Automatic configuration of multi-objective ACO algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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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 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. We use the bi-objective travelling salesman problem as a case study of the effect of algorithmic components and their possible interactions on performance. 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.