计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 155-161.doi: 10.19678/j.issn.1000-3428.0050049

• 人工智能及识别技术 • 上一篇    下一篇

基于改进平衡策略的多目标分解优化算法

李智翔,李赟,褚衍杰   

  1. 盲信号处理重点实验室,成都 610041
  • 收稿日期:2018-01-10 出版日期:2019-03-15 发布日期:2019-03-15
  • 作者简介:李智翔(1989—),男,博士研究生,主研方向为进化算法;李赟,工程师;褚衍杰,助理研究员。

Decomposition Multi-objective Optimization Algorithm Based on Improved Balancing Strategy

LI Zhixiang,LI Yun,CHU Yanjie   

  1. National Key Laboratory of Science and Technology on Blind Signal Processing,Chengdu 610041,China
  • Received:2018-01-10 Online:2019-03-15 Published:2019-03-15

摘要:

针对多目标优化算法在搜索中存在平衡解的收敛性和多样性问题,通过分析多目标分解进化算法,提出2种改进平衡策略。基于当前解和父代解的取值,设计繁殖算子,并与原有繁殖算子进行比较选出最优解。根据执行代数的不同,对邻居集合进行适应性调整。在此基础上,给出多目标分解进化算法。实验结果验证了2种平衡策略的有效性,同时该算法性能优于MOEA/D算法、NSGAII算法和IBEA算法。

关键词: 多目标优化, 进化计算, 分解方法, 平衡策略, 繁殖算子, 邻居集合

Abstract:

Aiming at the problem that the Multi-objective Optimization(MOP) algorithm has the convergence and diversity problem of the equilibrium solution in the search,two improved balancing strategies have been proposed by analyzing multi-objective decomposition evolutionary algorithms.Based on the values of the current solution and the parent solution,a breeding operator is designed and compared with the original breeding operator to select the optimal solution.The set of neighbors is adaptively changed according to the execution algebra.On this basis,a multi-objective decomposition evolution algorithm is given.Experimental results verify the effectiveness of the two balancing strategies,and the algorithm is better than the MOEA/D algorithm,NSGAII algorithm and IBEA algorithm.

Key words: Multi-objective Optimization(MOP), evolutionary computing, decomposition method, balancing strategy, reproduction operator, neighborhood set

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