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A resource allocation-based multi-objective evolutionary algorithm for large-scale multi-objective optimization

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Abstract

In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decision variables are contained. Therefore, it is a great challenge to address LSMOPs due to the curse of dimensionality. To tackle LSMOPs, this paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm. In the proposed algorithm, decision variables are firstly divided into convergence-related variables and diversity-related variables by the proposed layer thickness-based variable classification (LTVC) method. Then, a resource allocation-based convergence optimization strategy is introduced for the convergence-related variables, which can allocate more computational resource to the sub-component with the best contribution. For the diversity-related variables, diversity optimization technique is adopted. Finally, the experimental results verify that the proposed algorithm has a competitive performance compared with some state-of-the-art algorithms.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to part of this paper is still in the research stage but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors were supported by the National Nature Science Foundation of China under Grant No.61773106.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 62273080 and the 111 Project (B16009).

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Authors and Affiliations

  1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

    Wanting Yang, Jianchang Liu, Wei Zhang & **nnan Zhang

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Wanting Yang, Jianchang Liu, Wei Zhang & **nnan Zhang

  3. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, 110819, China

    Wanting Yang, Jianchang Liu, Wei Zhang & **nnan Zhang

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  1. Wanting Yang
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Correspondence to Jianchang Liu.

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Yang, W., Liu, J., Zhang, W. et al. A resource allocation-based multi-objective evolutionary algorithm for large-scale multi-objective optimization. Soft Comput 27, 17809–17831 (2023). https://doi.org/10.1007/s00500-023-09061-4

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