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Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal

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Abstract

Multi-objective algorithms are aimed to obtain a set of solutions, called Pareto set, covering the whole Pareto front, i.e. the representation of the optimal set of solutions. To this end, the algorithms should yield a wide amount of near-optimal solutions with a good diversity or spread along this front. This work presents a study on different coarse-grained distribution schemes dealing with Multi-Objective Ant Colony Optimization Algorithms (MOACOs). Two of them are a variation of independent multi-colony structures, respectively having a fixed number of ants in every subset or distributing the whole amount of ants into small sub-colonies. We introduce in this paper a third method: an island-based model where the colonies communicate by migrating ants, following a neighbourhood topology which fits to the search space. All the methods are aimed to cover the whole PF, thus each sub-colony or island tries to search for solutions in a limited area, complemented by the rest of colonies, in order to obtain a more diverse high-quality set of solutions. The models have been tested by implementing them considering three different MOACOs: two well-known and CHAC, an algorithm previously proposed by the authors. Three different instances of the bi-Criteria travelling salesman problem have been considered. The experiments have been performed in a parallel environment (inside a cluster platform), in order to get a time improvement. Moreover, the system scaling factor with respect to the number of processors will be also analysed. The results show that the proposed Pareto-island model and its novel neighbourhood topology performs better than the other models, yielding a more diverse and more optimized set of solutions. Moreover, from the algorithmic point of view, the proposed algorithm, named CHAC, yields the best results on average.

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Notes

  1. We have chosen this acronym instead of the extended MACS in order to distinguish the algorithm of the preceding MACS by Gambardella et al. (1999).

  2. Obviously the colonies does not find solutions below the Pareto front.

  3. The rest of the tradeoff study boxplots can be found at: http://geneura.ugr.es/~amorag/papers/i-moacos_soco.

  4. http://www.r-project.org.

  5. There are 9 approaches, so there are 8 degrees of freedom or possible values con compare with a given one.

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Acknowledgments

This work has been supported in part by HPC-Europa 2 project (with the support of the European Commission—Capacities Area—Research Infrastructures), and the P08-TIC-03903 project awarded by the Andalusian Regional Government, the FPU Grant 2009-2942, the TIN2011-28627-C04-02 project, the University of Granada PR-PP-2011-5 project and, in part, by the MUSES project (number 318508—FP7).

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  1. ATC Department, University of Granada, Granada, Spain

    A. M. Mora, P. García-Sánchez, J. J. Merelo & P. A. Castillo

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  1. A. M. Mora
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  2. P. García-Sánchez
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  3. J. J. Merelo
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Correspondence to A. M. Mora.

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Communicated by E. Alba.

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Mora, A.M., García-Sánchez, P., Merelo, J.J. et al. Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal. Soft Comput 17, 1175–1207 (2013). https://doi.org/10.1007/s00500-013-0993-y

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