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Improved Real Quantum Evolutionary Algorithm for Optimum Economic Load Dispatch with Non-convex Loads

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

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Abstract

An algorithm based on improved real quantum evolutionary algorithm (IRQEA) was developed to solve the problem of highly non-linear economic load dispatch problem with valve point loading. The performance of the proposed algorithm is evaluated on a test case of 15 units. The performance of the algorithm is compared with floating point genetic algorithm (FPGA) and real quantum evolutionary algorithm (RQEA). Results demonstrate that the performance of the IRQEA algorithm is far better than FPGA and RQEA algorithms in terms of convergence rate and solution quality.

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Authors and Affiliations

  1. Department of Electrical Engineering, NIT, Silchar, Assam, India, 788010

    Nidul Sinha, Kaustabh Moni Hazarika, Shantanu Paul, Himanshu Shekhar & Amrita Amrita Karmakar

Authors
  1. Nidul Sinha
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  2. Kaustabh Moni Hazarika
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  3. Shantanu Paul
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  4. Himanshu Shekhar
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  5. Amrita Amrita Karmakar
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Editor information

Editors and Affiliations

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

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

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© 2010 Springer-Verlag Berlin Heidelberg

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Sinha, N., Hazarika, K.M., Paul, S., Shekhar, H., Karmakar, A.A. (2010). Improved Real Quantum Evolutionary Algorithm for Optimum Economic Load Dispatch with Non-convex Loads. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_81

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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