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Interactive multiobjective evolutionary algorithm based on decomposition and compression

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Abstract

Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems (MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker (DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm (MOEA) called iDMOEA-εC, which utilizes the DM’s preferences to compress the objective space directly and progressively for identifying the DM’s preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions. DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM’s preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population. To ease the DM’s burden, only a small set of representative solutions is presented in each interaction to the DM, who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM’s preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.

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Acknowledgements

This work was supported in part by National Outstanding Youth Talents Support Program (Grant No. 61822304), National Natural Science Foundation of China (Grant No. 61673058), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (Grant No. U1609214), Consulting Research Project of the Chinese Academy of Engineering (Grant No. 2019-XZ-7), Projects of Major International (Regional) Joint Research Program of NSFC (Grant No. 61720106011), Peng Cheng Laboratory, and Beijing Advanced Innovation Center for Intelligent Robots and Systems.

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Lu Chen, Bin Xin & Jie Chen

  2. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing, 100081, China

    Bin Xin & Jie Chen

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  1. Lu Chen
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  2. Bin Xin
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  3. Jie Chen
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Correspondence to Bin Xin.

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Chen, L., Xin, B. & Chen, J. Interactive multiobjective evolutionary algorithm based on decomposition and compression. Sci. China Inf. Sci. 64, 202201 (2021). https://doi.org/10.1007/s11432-020-3092-y

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  • DOI: https://doi.org/10.1007/s11432-020-3092-y

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