App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Multi-objective flow direction algorithm with reference-direction selection and deep reinforcement learning-based generation

Zhang Yu
Liu Haiyan
School of Computing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0113

Abstract

Many multi-objective optimization problems suffer from three major challenges. Non-dominated candidate solutions are often difficult to distinguish. Search directions are not fully exploited. Population distribution may degenerate and the search may stagnate in the later stage of evolution. To address these issues, this paper proposes a multi-objective flow direction optimization algorithm based on reference-direction screening and deep reinforcement learning generation. The method introduces a candidate solution screening mechanism that combines reference-direction association information with the penalty-based boundary intersection scalar metric. This mechanism improves the discrimination of non-dominated candidate solutions. The algorithm embeds deep deterministic policy gradient into the offspring generation process. This strategy allows search actions to adapt to the current population evolution state. It enhances directional search capability and improves the response to different evolutionary stages. The method also designs a sparse-sector injection mechanism and a stagnation-burst mechanism. These two mechanisms combine sparse-region compensation with structural perturbation. They improve the uniformity of population coverage in the objective space and strengthen the sustained search ability in the later evolutionary stage. Experimental results show that, on the ZDT and DTLZ benchmark problems, the proposed algorithm achieves better results than four representative comparison algorithms on most test instances. The results demonstrate clear advantages in both convergence and diversity.

Foundation Support

国家自然科学基金青年基金项目(62002289)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0113
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-08-04] Accepted Paper

Cite This Article

张宇, 刘海燕. 基于参考方向筛选与深度强化学习生成的多目标流向算法 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0113. (Zhang Yu, Liu Haiyan. Multi-objective flow direction algorithm with reference-direction selection and deep reinforcement learning-based generation [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0113. )

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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