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Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation

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Abstract

The distribution of individuals in a population significantly influences convergence to global optimal solutions. However, determining how to maximise decision space information, which benefits convergence, is disregarded. This paper proposes a type of multi-objective evolutionary algorithm based on decision space partition (DSPEA), and designs the sphere initialisation strategies, initialisation method of individuals in each sphere, updating approach for the centroid, radius, and individuals of a hypersphere, and information sharing mechanism among spheres. The decision space in the DSPEA framework is explicitly divided into several hyperspheres. The non-dominated sorting genetic algorithm II is employed to implement each evolution of each hypersphere. An improvement approach related to the information sharing of the spheres is used to produce the future motions of the spheres by adopting particle swarm optimisation. Twelve problems were used to test the performance of DSPEA, and extensive experimental results show that DSPEA performs better than six state-of-the-art multi-objective evolutionary algorithms. Finally, DSPEA is used to optimise a hybrid power system. The results of the simulation optimisation tests on the parameters of the control strategy and the drive system for hybrid electric vehicles demonstrate that the proposed approach can obtain a set of improved solutions with low fuel consumption and pollutant emission.

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Acknowledgments

This work has been supported by National Natural Science Foundation of China (61540066 and 51475097), Science and Technology Foundation of Guizhou Province (R[2015]13, JZ[2014]2004, JZ[2014]2001, ZDZX[2013]6020, G[2014]4001, ZDZX[2014]6021), Science and Technology Foundation of Guizhou Province (J[2013]2127), Torch Program projects of Guizhou Province ([2013]5051), and National Key Technology Support Program of China (2012BAH62F00).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang, 550025, China

    Guanci Yang, Ansi Zhang, Shaobo Li, Qingsheng Xie & Ling He

  2. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, 74074, USA

    Guanci Yang

  3. Chengdu Institute of Computer Applications, University of the Chinese Academy of Sciences, Chengdu, Sichuan, 610041, China

    Yang Wang

  4. College of Finance, Guizhou University of Commerce, Guiyang, Guizhou, 550004, China

    Yunan Wang

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  1. Guanci Yang
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  2. Ansi Zhang
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  3. Shaobo Li
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  4. Yang Wang
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  5. Yunan Wang
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  6. Qingsheng Xie
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  7. Ling He
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Correspondence to Guanci Yang.

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Yang, G., Zhang, A., Li, S. et al. Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation. Appl Intell 46, 827–844 (2017). https://doi.org/10.1007/s10489-016-0864-1

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