A Cooperative Opposite-Inspired Learning Strategy for Ant-Based Algorithms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-03

AUTHORS

Nicolás Rojas-Morales , María-Cristina Riff , Carlos A. Coello Coello , Elizabeth Montero

ABSTRACT

In recent years, there has been an increasing interest in Opposite Learning strategies. In this work, we propose COISA, a Cooperative Opposite-Inspired Strategy for Ants. Inspired on the concept of anti-pheromone, in this approach, sub-colonies of ants perform different search processes to construct an initial pheromone matrix. We aim to produce a repel effect to (temporarily) avoid components that were related to an undesirable characteristic. To assess the effectiveness of COISA, we selected Ant Knapsack, a well-known ant-based algorithm that efficiently solves the Multidimensional Knapsack Problem. Results in benchmark instances show that the performance of Ant Knapsack is improved considering the opposite information, so that it can reach better solutions than before. More... »

PAGES

317-324

References to SciGraph publications

  • 2002-08-23. Anti-pheromone as a Tool for Better Exploration of Search Space in ANT ALGORITHMS
  • 2006. The Core Concept for the Multidimensional Knapsack Problem in EVOLUTIONARY COMPUTATION IN COMBINATORIAL OPTIMIZATION
  • 2008. Improving the Exploration Ability of Ant-Based Algorithms in OPPOSITIONAL CONCEPTS IN COMPUTATIONAL INTELLIGENCE
  • Book

    TITLE

    Swarm Intelligence

    ISBN

    978-3-030-00532-0
    978-3-030-00533-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-00533-7_25

    DOI

    http://dx.doi.org/10.1007/978-3-030-00533-7_25

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107362224


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