Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 22-28.DOI: 10.11772/j.issn.1001-9081.2020060891

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles     Next Articles

Decomposition based many-objective evolutionary algorithm based on minimum distance and aggregation strategy

LI Erchao, LI Kangwei   

  1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2020-05-30 Revised:2020-07-28 Online:2021-01-10 Published:2020-08-11
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61763026).


李二超, 李康伟   

  1. 兰州理工大学 电气工程与信息工程学院, 兰州 730050
  • 通讯作者: 李二超
  • 作者简介:李二超(1980-),男,河北保定人,教授,博士,主要研究方向:人工智能、多目标优化、机器人控制;李康伟(1994-),男,甘肃天水人,硕士研究生,主要研究方向:多目标优化。
  • 基金资助:

Abstract: Concerning the issue that the selection pressure of Pareto control based many-objective evolutionary algorithm is reduced when solving the problem of high-dimension and the diversity of the population is reduced of many-objective evolutionary algorithm based on decomposition when improving convergence and distribution, a decomposition based many-objective evolutionary algorithm based on minimum distance and aggregation strategy was proposed. Firstly, the angle decomposition based technique was used to decompose the target space into a specified number of subspaces in order to improve the diversity of population. Then, the method of cross neighborhood based on aggregation was added in the process of generating new solution, making the generated new solution closer to the parent solution. Finally, the convergence and distribution were improved by selecting solutions in each subspace based on minimum distance and aggregation strategy in two stages. In order to verify the feasibility of the algorithm, benchmark functions ZDT and DTLZ were used to conduct simulation experiments. The results show that the performance of the proposed algorithm is superior to those of the classical MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition), MOEA/D-DE (MOEA/D based on Differential Evolution), NSGA-Ⅲ (Nondominated Sorting Genetic Algorithms Ⅲ) and GrEA (Grid-based Evolutionary Algorithm). It can be seen that the proposed algorithm can effectively balance convergence and diversity while improving diversity.

Key words: evolutionary optimization algorithm, Many-objective Optimization Problem (MOP), convergence, diversity, distribution, decomposition

摘要: 针对基于帕累托(Pareto)支配的多目标进化算法在解决高维问题时选择压力降低,以及基于分解的多目标进化算法在提高收敛性和分布性的同时降低了种群多样性的问题,提出了一种基于最小距离和聚合策略的分解多目标进化算法。首先,使用基于角度分解的技术将目标空间分解为指定个数的子空间来提高种群的多样性;然后,在生成新解的过程中加入基于聚合的交叉邻域方法,使生成的新解更接近于父代解;最后,分两阶段在每个子空间内基于最小距离和聚合策略来选择解以提高收敛性和分布性。为了验证所提算法的可行性,采用标准测试函数ZDT和DTLZ进行仿真实验,结果表明所提算法的总体性能均优于经典的基于分解的多目标进化算法(MOEA/D)、MOEA/D-DE、NSGA-Ⅲ和GrEA。可见,所提算法在提高多样性的同时可以有效平衡收敛性和多样性。

关键词: 进化优化算法, 多目标优化问题, 收敛性, 多样性, 分布性, 分解

CLC Number: