多目標粒子群優化算法研究綜述

馮茜; 李擎; 全威; 裴軒墨

doi:10.13374/j.issn2095-9389.2020.10.31.001

多目標粒子群優化算法研究綜述

doi: 10.13374/j.issn2095-9389.2020.10.31.001

馮茜^{1, 2},
李擎^{1, 3, ,},
全威¹,
裴軒墨¹

1.
北京科技大學自動化學院，北京 100083
2.
華北理工大學機械工程學院，唐山 063210
3.
北京科技大學工業過程知識自動化教育部重點實驗室，北京 100083

基金項目: 國家自然科學基金資助項目（61673098）

詳細信息

通訊作者:
E-mail：liqing@ies.ustb.edu.cn

中圖分類號: TP18
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- 文章訪問數: 2943
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- 被引次數: 0
出版歷程
- 收稿日期: 2020-10-31
- 刊出日期: 2021-06-25

Overview of multiobjective particle swarm optimization algorithm

FENG Qian^{1, 2},
LI Qing^{1, 3
, ,},
QUAN Wei¹,
PEI Xuan-mo¹

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China
3.
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: liqing@ies.ustb.edu.cn

摘要

摘要: 針對多目標粒子群優化算法的研究進展進行綜述。首先，回顧了多目標優化和粒子群算法等基本理論；其次，分析了多目標優化所涉及的難點問題；再次，從最優粒子選擇策略，多樣性保持機制，收斂性提高手段，多樣性與收斂性平衡方法，迭代公式、參數、拓撲結構的改進方案5個方面綜述了近年來的最新成果；最后，指出多目標粒子群算法有待進一步解決的問題及未來的研究方向。
- 多目標優化 /
- 粒子群算法 /
- 收斂性 /
- 多樣性 /
- 進化算法
Abstract: In the real world, the development model of optimization problems tends to be diversified and large scale. Therefore, optimization technologies are facing severe challenges in terms of nonlinearity, multi-dimensionality, intelligence, and dynamic programming. Multiobjective optimization problems have multiple conflicting objective functions, so the unique optimal solution is impossible to obtain when optimizing, and multiple objective values must be considered to obtain a compromise optimal solution set. When traditional optimization methods treat complex multiobjective problems, such as those with nonlinearity and high dimensionality, good optimization results are difficult to ensure or even infeasible. The evolutionary algorithm is a method that simulates the natural evolution process and is optimized via group search technology. It has the characteristics of strong robustness and high search efficiency. Inspired by the foraging behavior of bird flocks in nature, the particle swarm optimization algorithm has a simple implementation, fast convergence, and unique updating mechanism. With its outstanding performance in the single-objective optimization process, particle swarm optimization has been successfully extended to multiobjective optimization, and many breakthrough research achievements have been made in combinatorial optimization and numerical optimization. Consequently, the multiobjective particle swarm algorithm has far-reaching research value in theoretical research and engineering practice. As a meta-heuristic optimization algorithm, particle swarm optimization is widely used to solve multiobjective optimization problems. This paper summarized the advanced strategies of the multiobjective particle swarm optimization algorithm. First, the basic theories of multiobjective optimization and particle swarm optimization were reviewed. Second, the difficult problems involving multiobjective optimization were analyzed. Third, the achievements in recent years were summarized from five aspects: optimal particle selection strategies, diversity maintenance mechanisms, convergence improvement measures, coordination methods between diversity and convergence, and improvement schemes of iterative formulas, parametric and topological structure. Finally, the problems to be solved and the future research direction of the multiobjective particle swarm optimization algorithm were presented.
- multiobjective optimization /
- particle swarm optimization algorithm /
- convergence /
- diversity /
- evolutionary algorithm

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圖 1 決策空間中粒子移動示意圖

Figure 1. Image of particle movement in the decision space

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