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Seed Disperser Ant Algorithm: An Evolutionary Approach for Optimization

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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

The Seed Disperser Ant Algorithm (SDAA) is inspired from the evolution of Seed Disperser Ant (Aphaenogaster senilis) colony. The ants in the colony are highly related siblings sharing average 75 % similarity in genotype. Hence, the genotype of every ant represents variables in binary form that are used to locally search for optimum solution. Once the colony matures, in other words a local optimum solution reached, nuptial flights take place where female genotype copies the male genotype originating from another colony. Once all colonies saturate new young queen emerges to establish new colonies. This diversifies the search for global optimum. The SDAA is validated by solving four 30 dimensional classical benchmark problems and six composite benchmark functions from CEC 2005 special session. The optimal results are found to be better than the selected state-of-the-art swarm intelligence based optimization.

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Acknowledgement

This work is supported by ER011-2013A, Ministry of Science, Technology and Innovation, Malaysia (MOSTI).

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Authors and Affiliations

  1. Nature Inspired Meta-heuristic Group, Department of Electrical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia

    Wen Liang Chang & Jeevan Kanesan

  2. Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International University, Lavale, Pune, Maharastra, India

    Anand Jayant Kulkarni

Authors
  1. Wen Liang Chang
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  2. Jeevan Kanesan
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  3. Anand Jayant Kulkarni
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Corresponding author

Correspondence to Jeevan Kanesan .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

Appendix A

Appendix A

Composite benchmark functions

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Chang, W.L., Kanesan, J., Kulkarni, A.J. (2015). Seed Disperser Ant Algorithm: An Evolutionary Approach for Optimization. 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_52

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  • DOI: https://doi.org/10.1007/978-3-319-16549-3_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16548-6

  • Online ISBN: 978-3-319-16549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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