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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2014: Swarm, Evolutionary, and Memetic Computing pp 14–26Cite as

Intelligent Water Drops Algorithm for Multimodal Spaces

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

Abstract

This paper presents a new nature inspired Intelligent Water Drops (IWD) based algorithm for finding peaks in continuous multimodal optimization problems. Initially various conceptual similarities were identified between IWD algorithm and Genetic Algorithm(GA). Simultaneously applying IWD-Continuous Optimization(IWD-CO) algorithm and GA on a function in finding the global optima and found IWD-CO having faster convergence qualities. By taking this as basis, GA has been replaced with IWD-CO in a recently developed Modified Roaming Optimization(MRO) algorithm and applied to various benchmark functions and found drastic variation in convergence. Results are proving that replacing GA with IWD-CO can be a novel step in evolutionary based multimodal search algorithms.

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Author information

Authors and Affiliations

  1. Rajiv Gandhi University of Knowledge Technologies, Basara, 504107, India

    Harish Y., Venkateshwarlu B., Chakravarthi Jada, Kranthi Kumar R. & Irfan Feroz G.

Authors
  1. Harish Y.
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  2. Venkateshwarlu B.
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  3. Chakravarthi Jada
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  4. Kranthi Kumar R.
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  5. Irfan Feroz G.
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Corresponding author

Correspondence to Venkateshwarlu B. .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Y., H., B., V., Jada, C., R., K.K., G., I.F. (2015). Intelligent Water Drops Algorithm for Multimodal Spaces. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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