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Combining modified inverted generational distance indicator with reference-vector-guided selection for many-objective optimization

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Abstract

The modified inverted generational distance (IGD+) indicator has been widely used to handle optimization problems with two or three objectives due to its ability to obtain weak Pareto optimal solutions. However, only using the IGD+ indicator cannot effectively balance the convergence and diversity of candidate solutions in the high-dimensional objective space of many-objective optimization problems (MaOPs). To solve this issue, we propose a two-stage selection strategy based on the IGD+ indicator and the reference vector guidance method. This two-stage selection mechanism uses the IGD+ indicator and the reference vector guidance method to select two sub-populations, which form the parent population at the next generation. In this way, it can balance convergence and diversity well when solving MaOPs. Experiments were performed on 65 test problems. The proposed algorithm achieved the best HV value 39 times, showing competitive performance compared to five representative algorithms for many-objective optimization.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 61903003 and 61873002; in part by the Open Project of Anhui Province Key Laboratory of Special and Heavy Load Robot under Grant TZJQR001 − 2021; in part by the Nature Science Research Project of Anhui province under Grant 2008085QE227; and in part by the Scientific Research Projects in Colleges and Universities of Anhui Province under Grant KJ2019A0051.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, China

    Fei Li, Zhengkun Shang & Hao Shen

  2. Ma’anshan University, Ma’anshan, 243100, China

    Fei Li

  3. Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma’anshan, 243032, China

    Fei Li

  4. Department of Automation, North China Electric Power University, Baoding, 071003, China

    Yuanqu Liu

  5. School of Automation, Central South University, Changsha, 410083, China

    Pei-Qiu Huang

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  1. Fei Li
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Correspondence to Fei Li.

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Li, F., Shang, Z., Shen, H. et al. Combining modified inverted generational distance indicator with reference-vector-guided selection for many-objective optimization. Appl Intell 53, 12149–12162 (2023). https://doi.org/10.1007/s10489-022-04115-w

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