An artificial bee colony algorithm for the maximally diverse grouping problem

  • Authors:

  • Francisco J. Rodriguez;M. Lozano;C. GarcíA-MartíNez;Jonathan D. GonzáLez-Barrera

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computing and Numerical Analysis, University of Córdoba, Córdoba, Spain;Department of Statistics, Operational Research, and Computer Science, University of La Laguna, San Cristobal de La Laguna, Spain

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees. The behaviour of this algorithm is determined by two search strategies: an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem.