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Cooperative Model of Evolutionary Algorithms and Real-World Problems

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Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (SEMCCO 2019, FANCCO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1092))

Abstract

A cooperative model of efficient evolutionary algorithms is proposed and studied when solving 22 real-world problems of the CEC 2011 benchmark suite. Four adaptive algorithms are chosen for this model, namely the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) and three variants of adaptive Differential Evolution (CoBiDE, jSO, and IDEbd). Five different combinations of cooperating algorithms are tested to obtain the best results. Although the two algorithms use constant population size, the proposed model employs an efficient linear population-size reduction mechanism. The best performing Cooperative Model of Evolutionary Algorithms (CMEAL) employs two EAs, and it outperforms the original algorithms in 10 out of 22 real-world problems.

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Authors and Affiliations

  1. University of Ostrava, 30. dubna 22, 70200, Ostrava, Czech Republic

    Petr Bujok

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  1. Petr Bujok
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Correspondence to Petr Bujok .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Aleš Zamuda

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Division of Control and Instrumentation, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Bujok, P. (2020). Cooperative Model of Evolutionary Algorithms and Real-World Problems. In: Zamuda, A., Das, S., Suganthan, P., Panigrahi, B. (eds) Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. SEMCCO FANCCO 2019 2019. Communications in Computer and Information Science, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-37838-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-37838-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37837-0

  • Online ISBN: 978-3-030-37838-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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