Future Generation Computer Systems
Bi-Criterion Optimization with Multi Colony Ant Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion 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
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
An analysis of local search for the bi-objective bidimensional knapsack problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Multi-objective ant colony optimization (MOACO) algorithms have shown promising results for various multi-objective problems, but they also offer a large number of possible design choices. Often, exploring all possible configurations is practically infeasible. Recently, the automatic configuration of a MOACO framework was explored and was shown to result in new state-of-the-art MOACO algorithms for the bi-objective traveling salesman problem. In this paper, we apply this approach to the bi-objective bidimensional knapsack problem (bBKP) to prove its generality and power. As a first step, we tune and improve the performance of four MOACO algorithms that have been earlier proposed for the bBKP. In a second step, we configure the full MOACO framework and show that the automatically configured MOACO framework outperforms all previous MOACO algorithms for the bBKP as well as their improved variants.