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A General Intelligent Optimization Algorithm Combination Framework with Application in Economic Load Dispatch Problems

Author

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  • Jinghua Zhang

    (Hebei Engineering Research Center of Simulation Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)

  • Ze Dong

    (Hebei Engineering Research Center of Simulation Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China)

Abstract

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework.

Suggested Citation

  • Jinghua Zhang & Ze Dong, 2019. "A General Intelligent Optimization Algorithm Combination Framework with Application in Economic Load Dispatch Problems," Energies, MDPI, vol. 12(11), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2175-:d:237944
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    References listed on IDEAS

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    1. Jiangtao Yu & Chang-Hwan Kim & Abdul Wadood & Tahir Khurshiad & Sang-Bong Rhee, 2018. "A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 11(8), pages 1-25, July.
    2. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
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    Cited by:

    1. El-Sayed, Wael T. & El-Saadany, Ehab F. & Zeineldin, Hatem H. & Al-Sumaiti, Ameena S., 2020. "Fast initialization methods for the nonconvex economic dispatch problem," Energy, Elsevier, vol. 201(C).

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