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Hybridizing Differential Evolution Variants Through Heterogeneous Mixing in a Distributed Framework

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Hybrid Soft Computing Approaches

Part of the book series: Studies in Computational Intelligence ((SCI,volume 611))

Abstract

While hybridizing the complementary constituent soft computing techniques has displayed improved efficacy, the hybridization of complementary characteristics of different Differential Evolution (DE) variants (could as well be extended to evolutionary algorithms variants in general) through heterogeneous mixing in a distributed framework also holds a great potential. This chapter proposes to mix competitive DE variants with diverse characteristics in a distributed framework as against the typical distributed (homogeneous) Differential Evolution (dDE) algorithms found in DE literature. After an empirical analysis of 14 classical DE variants on 14 test functions, two heterogeneous dDE frameworks dDE_HeM_best and dDE_HeM_worst obtained by mixing best DE variants and worst DE variants, respectively, have been realized, implemented and tested on the benchmark optimization problems. The simulation results have validated the robustness of the heterogeneous mixing of best variants. The chapter also hybridized DE and dynamic DE variants in a distributed framework. The robustness of the resulting framework has been validated by benchmarking it against the state-of-the-art DE algorithms in the literature.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    G. Jeyakumar & C. Shunmuga Velayutham

Authors
  1. G. Jeyakumar
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  2. C. Shunmuga Velayutham
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Correspondence to G. Jeyakumar .

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Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, India

    Susanta Chakraborty

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Jeyakumar, G., Shunmuga Velayutham, C. (2016). Hybridizing Differential Evolution Variants Through Heterogeneous Mixing in a Distributed Framework. In: Bhattacharyya, S., Dutta, P., Chakraborty, S. (eds) Hybrid Soft Computing Approaches. Studies in Computational Intelligence, vol 611. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2544-7_4

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  • DOI: https://doi.org/10.1007/978-81-322-2544-7_4

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2543-0

  • Online ISBN: 978-81-322-2544-7

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