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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Sparse large-scale multi-objective optimization algorithm incorporating dynamic fuzzy perturbation and feature guidance

Li Tong1a,1b
Gu Qinghua1a,1b
Wang Qian1a,1b
Luo Jiale1b,1c
Wang Jianguo2
1. a. College of Resources Engineering, b. Xi'an Key Laboratory for Intelligent Industrial Perception, Calculation & Decision, c. College of Management, Xi'an University of Architecture & Technology, Xi'an 710055, China
2. Hami Hexiang Industry & Trade Co. , Ltd. , Hami Xinjiang 839200, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0113

Abstract

Existing multi-objective evolutionary algorithms face challenges including weak sparsity control, poor balance in resolving objective conflicts, and susceptibility to local optima when solving large-scale sparse multi-objective optimization problems. This paper proposed a multi-objective evolutionary algorithm incorporating dynamic fuzzy perturbation and feature-guided adaptive crossover (MOEA-FA) to address these issues. It first employed a dynamic fuzzy perturbation strategy with dual-phase parameter adaptation to guide the evolutionary population towards specific preference regions for obtaining decision-maker-desired solutions, balancing objective conflicts and preventing premature convergence. Additionally, the algorithm adopted a feature-guided adaptive crossover strategy for binary variables, identifying critical features based on variable activation frequency and prioritizing the retention of high-contribution variables to maintain solution sparsity. To validate MOEA-FA's effectiveness, the study evaluated it against six state-of-the-art algorithms on eight benchmark problems (SMOP) and portfolio optimization problems. Experimental results showed MOEA-FA achieved the best IGD values on 80% of the test problems and the best HV values on 82.86% of the test problems. These results demonstrate MOEA-FA offers superior performance for solving large-scale sparse multi-objective optimization problems.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0113
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-18] Accepted Paper

Cite This Article

李彤, 顾清华, 王倩, 等. 融合动态模糊扰动和特征引导的稀疏大规模多目标优化算法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0113. (Li Tong, Gu Qinghua, Wang Qian, et al. Sparse large-scale multi-objective optimization algorithm incorporating dynamic fuzzy perturbation and feature guidance [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0113. )

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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