Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 15-21.DOI: 10.11772/j.issn.1001-9081.2020060908

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

Multi-objective estimation of distribution algorithm with adaptive opposition-based learning

LI Erchao, YANG Rongrong   

  1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2020-05-30 Revised:2020-07-29 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, 杨蓉蓉   

  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,河北定州人,教授,博士,主要研究方向:人工智能、多目标优化、机器人控制;杨蓉蓉(1994-),女,甘肃庆阳人,硕士研究生,主要研究方向:多目标优化。
  • 基金资助:

Abstract: Aiming at the defect of poor global convergence of the regularity model-based multi-objective estimation of distribution algorithm, a multi-objective estimation of distribution algorithm based on adaptive Opposition-Based Learning (OBL) was proposed. In the algorithm, whether to carry out OBL was judged according to the change rate of the function. When the change rate of the function was small, the algorithm was easily to fall into the local optimum, so that OBL was performed to increase the diversity of individuals in current population. When the change rate of the function was large, the regularity model-based multi-objective estimation of distribution algorithm was run. In the proposed algorithm, with the timely introduction of OBL strategy, the influences of population diversity and individual distribution on the overall convergence quality and speed of optimization algorithm were reduced. In order to verify the performance of the improved algorithm, Regularity Model-based Multi-objective Estimation of Distribution Algorithm (RM-MEDA), Hybrid Wading across Stream Algorithm-Estimation Distribution Algorithm (HWSA-EDA) and Inverse Modeling based multiObjective Evolutionary Algorithm (IM-MOEA) were selected as comparison algorithms to carry out the test with the proposed algorithm on ZDT and DTLZ test functions respectively. The test results show that the proposed algorithm not only has good global convergence, but also improves the distribution and uniformity of solutions except on DTLZ2 function.

Key words: Multi-objective Optimization Problem (MOP), local optimum, Opposition-Based Learning (OBL), population diversity, convergence

摘要: 针对基于规则模型的多目标分布估计算法全局收敛性较弱的缺陷,提出了一种基于自适应反向学习(OBL)的多目标分布估计算法。该算法根据函数变化率的大小来决定是否进行OBL:当函数变化率较小时,算法可能陷入局部最优,所以进行OBL以提高当前种群中个体的多样性;当函数变化率较大时,运行基于规则模型的多目标分布估计算法。所提算法通过适时地引入OBL策略,减小了种群多样性及个体的分布情况对优化算法整体收敛质量以及收敛速度的影响。为了验证改进算法的性能,选取基于规则模型的多目标分布估计算法(RM-MEDA)、摸石头过河算法与分布估计混合算法(HWSA-EDA)以及基于逆建模的多目标进化算法(IM-MOEA)作为对比算法与所提算法分别在ZDT和DTLZ测试函数上进行测试。测试结果表明,除了在DTLZ2函数上以外,所提算法不仅有良好的全局收敛性,而且解的分布性和均匀性都有所提高。

关键词: 多目标优化问题, 局部最优, 反向学习, 种群多样性, 收敛性

CLC Number: