Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions

  • Authors:

  • Akbar Karimi;Hadi Nobahari;Patrick Siarry

  • Affiliations:

  • Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran;Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran;Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université Paris 12 Val-de-Marne, Créteil, France 94010

  • Venue:
  • Computational Optimization and Applications
  • Year:
  • 2010

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Abstract

A new hybrid optimization method, combining Continuous Ant Colony System (CACS) and Tabu Search (TS) is proposed for minimization of continuous multi-minima functions. The new algorithm incorporates the concepts of promising list, tabu list and tabu balls from TS into the framework of CACS. This enables the resultant algorithm to avoid bad regions and to be guided toward the areas more likely to contain the global minimum. New strategies are proposed to dynamically tune the radius of the tabu balls during the execution and also to handle the variable correlations. The promising list is also used to update the pheromone distribution over the search space. The parameters of the new method are tuned based on the results obtained for a set of standard test functions. The results of the proposed scheme are also compared with those of some recent ant based and non-ant based meta-heuristics, showing improvements in terms of accuracy and efficiency.