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Applying Ant Colony Optimization to Dynamic Binary-Encoded Problems

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Applications of Evolutionary Computation (EvoApplications 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9028))

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Abstract

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the initial developments of the introduced to address binary-encoded DOPs and compared with existing EAs. The experimental results show that ACO with an appropriate pheromone evaporation rate outperforms EAs in most dynamic test cases.

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Notes

  1. 1.

    Similar update policy with the existing BAA [16].

  2. 2.

    This pheromone update policy is the one described in Sect. 2 and finally associated with \(\text {ACO}_{\mathbb {B}}\).

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Acknowledgement

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.

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Authors and Affiliations

  1. School of Computer Science and Informatics, Centre for Computational Intelligence (CCI), De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Michalis Mavrovouniotis & Shengxiang Yang

Authors
  1. Michalis Mavrovouniotis
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  2. Shengxiang Yang
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Correspondence to Michalis Mavrovouniotis .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

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Mavrovouniotis, M., Yang, S. (2015). Applying Ant Colony Optimization to Dynamic Binary-Encoded Problems. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_68

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  • DOI: https://doi.org/10.1007/978-3-319-16549-3_68

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-16549-3

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