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The continuous differential ant-stigmergy algorithm for numerical optimization

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  • Peter Korošec
  • Jurij Šilc

Abstract

Many promising optimization algorithms for solving numerical optimization problems come from population-based metaheuristics. A few of them are based on Swarm-Intelligence Algorithms, which are inspired by the collective behavior of social organisms. One of the most successful of such algorithms is the Differential Ant-Stigmergy Algorithm (DASA), which uses stigmergy, a method of communication in emergent systems where the individual parts (artificial ants) of the system communicate with one another by modifying their local environment (pheromone intensity). The main characteristic of the DASA is its underlying structure (pheromone graph) that uses discrete steps to move through a continuous search space. As a consequence of this the search-space movement is in some way limited and the algorithm’s time/space complexity is increased. In order to overcome the problem an improved algorithm called the Continuous Differential Ant-Stigmergy Algorithm (CDASA) is proposed and then benchmarked on standard benchmark functions. This benchmarking showed that the CDASA performs better than the DASA, especially at lower dimensions, that its time/space complexity is decreased, and that the algorithm code is simplified. As such, the CDASA is more suitable for parallel implementations on General-Purpose Graphic Processing Units. Compared to the Swarm-Intelligence Algorithms presented in this paper, the CDASA is the best-performing algorithm and competitive to the state-of-the-art algorithms belonging to different metaheuristic approaches. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Peter Korošec & Jurij Šilc, 2013. "The continuous differential ant-stigmergy algorithm for numerical optimization," Computational Optimization and Applications, Springer, vol. 56(2), pages 481-502, October.
    • Handle: RePEc:spr:coopap:v:56:y:2013:i:2:p:481-502
    DOI: 10.1007/s10589-013-9561-8
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    References listed on IDEAS

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    1. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    2. Akbar Karimi & Hadi Nobahari & Patrick Siarry, 2010. "Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions," Computational Optimization and Applications, Springer, vol. 45(3), pages 639-661, April.
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