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Solving large-scale multiobjective optimization via the probabilistic prediction model

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Abstract

The characteristic of large-scale multiobjective optimization problems (LSMOPs) is optimizing multiple conflicting objectives while considering thousands of decision variables at the same time. An efficient optimization algorithm for LSMOPs should have the ability to search a large decision space and find the global optimum in the objective space. Maintaining the diversity of the population is one of the effective ways to locate the Pareto optimal set in a large search space. In this paper, we propose a large-scale multiobjective optimization algorithm based on the probabilistic prediction model, called LMOPPM, to establish a generating-filtering strategy and tackle the LSMOP. The proposed method improves the diversity of the population through importance sampling and enhances the convergence of the population via a trend prediction model. Furthermore, due to the adoption of the individual-based evolutionary mechanism, the computational costs of the proposed method are less relevant to the number of decision variables, thus avoiding high time complexity. We compared the proposed algorithm with several state-of-the-art algorithms on benchmark functions. The experimental results and complexity analysis demonstrate that the proposed algorithm provides significant improvements in terms of performance and computational efficiency in large-scale multiobjective optimization.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61673328).

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Authors and Affiliations

  1. Department of Artificial Intelligence, Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism, School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China

    Haokai Hong, Kai Ye, Min Jiang & Donglin Cao

  2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Kay Chen Tan

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  1. Haokai Hong
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  2. Kai Ye
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  4. Donglin Cao
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Correspondence to Min Jiang or Donglin Cao.

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This work was supported in part by the National Natural Science Foundation of China under Grant 61673328, and in part by the Collaborative Project Foundation of Fuzhou-Xiamen-Quanzhou Innovation Demonstration Zone under Grant 3502ZCQXT202001.

This article is a journal version of Reference [1].

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Hong, H., Ye, K., Jiang, M. et al. Solving large-scale multiobjective optimization via the probabilistic prediction model. Memetic Comp. 14, 165–177 (2022). https://doi.org/10.1007/s12293-022-00358-9

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