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A unified ant colony optimization algorithm for continuous optimization

Author

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  • Liao, Tianjun
  • Stützle, Thomas
  • Montes de Oca, Marco A.
  • Dorigo, Marco

Abstract

In this article, we propose UACOR, a unified ant colony optimization (ACO) algorithm for continuous optimization. UACOR includes algorithmic components from ACOR,DACOR and IACOR-LS, three ACO algorithms for continuous optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR’s flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC’05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC’05 set. These results show the high potential ACO algorithms have for continuous optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art continuous optimizers.

Suggested Citation

  • Liao, Tianjun & Stützle, Thomas & Montes de Oca, Marco A. & Dorigo, Marco, 2014. "A unified ant colony optimization algorithm for continuous optimization," European Journal of Operational Research, Elsevier, vol. 234(3), pages 597-609.
    • Handle: RePEc:eee:ejores:v:234:y:2014:i:3:p:597-609
    DOI: 10.1016/j.ejor.2013.10.024
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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.
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    2. Stefano Bromuri, 2019. "Dynamic heuristic acceleration of linearly approximated SARSA( $$\lambda $$ λ ): using ant colony optimization to learn heuristics dynamically," Journal of Heuristics, Springer, vol. 25(6), pages 901-932, December.
    3. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
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    6. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.

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