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Optimal operator selection based on the hybrid operator selection strategy

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Abstract

At present, the multiobjective optimization algorithm based on decomposition (MOEA/D) has become a typical algorithm for solving multiobjective problems. Following extensive experiments, researchers found that the choice of mutation operators in MOEA/D has a great influence on the quality of the solutions, and a suitable operator can largely improve the algorithm’s performance. This paper proposes a hybrid operator selection strategy based on feedback and support vector machine (SVM) classification, and applies it to the framework of MOEA/D. The algorithm divides the whole evolutionary process into several stages, and each stage is divided into two parts. In the first part, multiple mutation operators are given the same computing resources to generate offspring, then, we test the performance of each operator and elect the operator with the best performance. Meanwhile, an SVM classifier is trained by taking the truly evaluated offspring as the training set. Good operators do not always generate good offspring. Therefore, in the second part, we use the best performing operator selected in the first part to generate offspring, while using the trained classifier to filter all the newly generated solutions. The real evaluation should be carried out only when the offspring are identified as positive. Otherwise, it should be ignored to save the real evaluation computing resources. Additionally, the number of all positive individuals is recorded in the second part, which is when we decide whether to continue using this operator at the next stage or to reselect a more suitable operator. Experimental results verify the effectiveness of this strategy.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The work is partially supported by the National Natural Science Foundation of China (Nos. U1836216, 61702310, 61772322), the major fundamental research project of Shandong, China(No. ZR2020MF042, ZR2019ZD03), and the Taishan Scholar Project of Shandong, China.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Hongling Chen, Yanyan Tan, Zeyuan Yan, Lili Meng & Wenbo Wan

  2. Institute of Data Science and Technology, Shandong Normal University, Jinan, 250358, China

    Yanyan Tan, Lili Meng & Wenbo Wan

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  1. Hongling Chen
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  2. Yanyan Tan
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  3. Zeyuan Yan
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  4. Lili Meng
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  5. Wenbo Wan
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Correspondence to Yanyan Tan.

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Chen, H., Tan, Y., Yan, Z. et al. Optimal operator selection based on the hybrid operator selection strategy. Memetic Comp. 14, 461–476 (2022). https://doi.org/10.1007/s12293-022-00382-9

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