Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1101-1107,1118

Dynamic multi-objective optimization method based on hybrid prediction strategy and improved social learning optimization algorithm

Zhang Jiea
Ma Feifeib
Zheng Hedana
Liu Zhizhonga
a. School of Computer & Control Engineering, b. Center for Network Security & Information, Yantai University, Yantai Shandong 264005, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0453

Abstract

Recently, some researches have been carried out on dynamic multi-objective optimization algorithm based on prediction, and a series of effective algorithms have been proposed. However, existing methods usually adopt a single prediction strategy, make the algorithm unable to effectively cope with drastic environmental changes. To tackle the above issues, this paper proposed a dynamic multi-objective optimization method based on hybrid prediction strategy and improved social learning optimization algorithm. Firstly, when the environment changes, this method generated a part of the group based on the representative individual prediction strategy, and generated some new groups based on the inflection point prediction strategy. In particular, to maintain the diversity of the population, it randomly generated a certain number of new individuals according to the historical information of the algorithm iteration and the environmental changes. To improve the efficiency of solving the problem, this paper improved the social learning optimization algorithm and designed the suitable operators for dynamic multi-objective optimization problems in three evolution space. Finally, it formed a new dynamic multi-objective optimization method by combining the hybrid prediction strategy with the improved social learning optimization algorithm. This paper used FDA, DMOP and F function sets as experimental test function sets, and deeply analyzed the performance of the proposed method with IGD. Experimental results show that the proposed method has good convergence and robustness.

Foundation Support

国家自然科学基金资助项目(61872126,62273290)
山东省自然科学基金重点项目(ZR2020KF019)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0453
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1101-1107,1118
Serial Number: 1001-3695(2023)04-023-1101-07

Publish History

[2022-11-14] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

张杰, 马菲菲, 郑禾丹, 等. 基于混合预测策略与改进社会学习优化算法的动态多目标优化方法 [J]. 计算机应用研究, 2023, 40 (4): 1101-1107,1118. (Zhang Jie, Ma Feifei, Zheng Hedan, et al. Dynamic multi-objective optimization method based on hybrid prediction strategy and improved social learning optimization algorithm [J]. Application Research of Computers, 2023, 40 (4): 1101-1107,1118. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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