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
Ant Colony Optimization for Multi-Objective Optimization Problems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 12th annual conference on Genetic and evolutionary computation
EA'09 Proceedings of the 9th international conference on Artificial evolution
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Automatic design of ant algorithms with grammatical evolution
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Calibrating continuous multi-objective heuristics using mixture experiments
Journal of Heuristics
S-Race: a multi-objective racing algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In the last few years a significant number of ant colony optimization (ACO) algorithms have been proposed for tackling multi-objective optimization problems. In this paper, we propose a software framework that allows to instantiate the most prominent multi-objective ACO (MOACO) algorithms. More importantly, the flexibility of this MOACO framework allows the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that such an automatic configuration of MOACO algorithms is highly desirable, given that our automatically configured algorithms clearly outperform the best performing MOACO algorithms that have been proposed in the literature. As far as we are aware, this paper is also the first to apply automatic algorithm configuration techniques to multi-objective stochastic local search algorithms.