Abstract
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.
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Bezerra, L.C.T., López-Ibáñez, M., Stützle, T. (2012). Automatic Generation of Multi-objective ACO Algorithms for the Bi-objective Knapsack. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_4
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DOI: https://doi.org/10.1007/978-3-642-32650-9_4
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