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Self-adaptive Ensemble Differential Evolution with Sampled Parameter Values for Unit Commitment

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9873))

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Abstract

In literature, empirically and theoretically, it has been well-demonstrated that the performance of differential evolution (DE) is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. According to the No Free Lunch theorem, a single set of well-tuned combination of strategies and their associated parameter combination is not suitable for optimization problems having different characteristics. In addition, different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. Based on this observation, DE with an ensemble of mutation and crossover strategies and their associated control parameters referred to as EPSDE was proposed. However, it has been observed that the fixed discrete parameter values as in EPSDE may not yield optimal performance. In this paper, we propose self-adaptive DE algorithm (Sa-EPSDE) with a set of mutation strategies while their associated parameter values F and CR are sampled using mean and standard deviation values. In addition, the probability of selecting a combination to produce an offspring at a particular generation during the evolution process depends on the success of the combination. The performance of the proposed Sa-EPSDE algorithm is evaluated on a set of 14 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005. In order to validate the performance of proposed Sa-EPSDE algorithm on real-world applications, the algorithm is hybridized with a simple priority listing method and applied to solve unit commitment problem by considering 10-, 20-, 40-, 60-, 80- and 100-bus systems for one day scheduling period. The results showed that the proposed method obtained superior performance against other compared algorithms.

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Acknowledgement

This work was supported by the Singapore National Research Foundation (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and Cambridge Advanced Research Centre in Energy Efficiency in Singapore (CARES), C4T project.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Nandar Lynn & Ponnuthurai Nagaratnam Suganthan

  2. School of Electronics Engineering, Kyungpook National University, Daegu, South Korea

    Rammohan Mallipeddi

Authors
  1. Nandar Lynn
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  2. Rammohan Mallipeddi
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  3. Ponnuthurai Nagaratnam Suganthan
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Corresponding author

Correspondence to Ponnuthurai Nagaratnam Suganthan .

Editor information

Editors and Affiliations

  1. IIT, New Dehli, India

    Bijaya Ketan Panigrahi

  2. Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  4. Department of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

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Lynn, N., Mallipeddi, R., Suganthan, P.N. (2016). Self-adaptive Ensemble Differential Evolution with Sampled Parameter Values for Unit Commitment. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48958-2

  • Online ISBN: 978-3-319-48959-9

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