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An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem

Author

Listed:
  • M. A. El-Shorbagy

    (Prince Sattam bin Abdulaziz University
    Menoufia University)

  • A. A. Mousa

    (Menoufia University
    Taif University)

  • M. A. Farag

    (Menoufia University)

Abstract

A new intelligent computing based approach for solving multi-objective unit commitment problem (MOUCP) and its fuzzy model is presented in this paper. The proposed intelligent approach combines binary-real-coded genetic algorithm (BRCGA) and K-means clustering technique to find the optimal schedule of the generation units in MOUCP. BRCGA is used in order to tackle both the unit scheduling and load dispatch problems. While, K-means clustering technique is used to divide the population into a specific number of subpopulation with-dynamic-sizes. In this way, different genetic algorithm (GA) operators can apply to each sub-population, instead of using the same GA operators for all population. The proposed intelligent algorithm has been tested on standard systems of MOUCPs. The results showed the efficiency of the proposed approach to solve (MOUCP) and its fuzzy model.

Suggested Citation

  • M. A. El-Shorbagy & A. A. Mousa & M. A. Farag, 2019. "An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 911-944, September.
    • Handle: RePEc:spr:opsear:v:56:y:2019:i:3:d:10.1007_s12597-019-00388-x
    DOI: 10.1007/s12597-019-00388-x
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    References listed on IDEAS

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    1. Yang, Zhile & Li, Kang & Guo, Yuanjun & Feng, Shengzhong & Niu, Qun & Xue, Yusheng & Foley, Aoife, 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles," Energy, Elsevier, vol. 170(C), pages 889-905.
    2. M.A El-Shorbagy & A.A Mousa & M Farag, 2017. "Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique," Review of Computer Engineering Research, Conscientia Beam, vol. 4(1), pages 11-29.
    3. El-Shorbagy, M.A. & Mousa, A.A. & Nasr, S.M., 2016. "A chaos-based evolutionary algorithm for general nonlinear programming problems," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 8-21.
    4. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    5. M. A. El-Shorbagy & A. Y. Ayoub & A. A. Mousa & I. M. El-Desoky, 2019. "An enhanced genetic algorithm with new mutation for cluster analysis," Computational Statistics, Springer, vol. 34(3), pages 1355-1392, September.
    6. M.A. El-Shorbagy & A.A. Mousa & M. Farag, 2017. "Solving Nonlinear Single-Unit Commitment Problem by Genetic Algorithm Based Clustering Technique," Review of Computer Engineering Research, Conscientia Beam, vol. 4(1), pages 11-29.
    7. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    8. Melamed, Michal & Ben-Tal, Aharon & Golany, Boaz, 2018. "A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source," Renewable Energy, Elsevier, vol. 118(C), pages 909-917.
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