Automatic Generation of Multi-objective ACO Algorithms for the Bi-objective Knapsack

  • Leonardo C. T. Bezerra
  • Manuel López-Ibáñez
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)


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.


Nondominated Solution Heuristic Information Multidimensional Knapsack Problem Pheromone Deposit Pheromone Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leonardo C. T. Bezerra
    • 1
  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

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