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A gene-level hybrid search framework for multiobjective evolutionary optimization

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Abstract

This paper proposes a general framework of gene-level hybrid search (GLHS) for multiobjective evolutionary optimization. Regarding the existing hybrid search methods, most of them usually combine different search strategies and only select one search strategy to generate child solution. This kind of hybrid search is called as a chromosome-level approach in this paper. However, in GLHS, every gene bit of the child solution can be produced using different search strategies and such operation provides the enhanced exploration capability. As an example, two different DE mutation strategies are used in this paper as the variance candidate pool to implement the proposed GLHS framework, named GLHS-DE. To validate the effectiveness of GLHS-DE, it is embedded into one state-of-the-art algorithmic framework of MOEA/D, and is compared to a basic DE operator and two competitive hybrid search operators, i.e., FRRMAB and CDE, on 80 test problems with two to fifteen objectives. The experimental results show GLHS-DE obtains a superior performance over DE, FRRMAB and CDE on about 70 out of 80 test problems, indicating the promising application of our approach for multiobjective evolutionary optimization.

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Acknowledgements

This work was supported by the Joint Funds of the National Natural Science Foundation of China under Key Program under Grant U1713212, the National Natural Science Foundation of China under Grant 61672358, the Natural Science Foundation of Guangdong Province under Grant 2017A030313338, and the Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grants JCYJ20170817102218122 and JCYJ20170302154032530.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Qingling Zhu, Qiuzhen Lin & Jianyong Chen

  2. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingling Zhu

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  1. Qingling Zhu
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  2. Qiuzhen Lin
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  3. Jianyong Chen
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Correspondence to Qiuzhen Lin.

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Zhu, Q., Lin, Q. & Chen, J. A gene-level hybrid search framework for multiobjective evolutionary optimization. Neural Comput & Applic 30, 759–773 (2018). https://doi.org/10.1007/s00521-018-3563-5

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