一种基于新型邻域更新策略的MOEA/D算法

MOEA/D Algorithm Based on New Neighborhood Updating Strategy

针对MOEA/D算法求解复杂优化问题时,邻域更新策略的无限制替换易造成种群多样性缺失的问题,提出了一种基于新型邻域更新策略的MOEA/D算法(MOEA/D-ENU)。该算法在进化过程中对解的信息进行充分挖掘,按照邻域更新能力对产生的新解进行分类,并针对不同类型的新解,自适应地采取不同的邻域更新策略,在保证种群收敛速度的同时,又兼顾了种群的多样性。实验中,选取ZDT,UF,CF等9个函数作为标准测试集,将改进后的算法MOEA/D-ENU与其他5种算法进行对比实验,并以IGD和HV为评估指标。实验结果表明新算法具有更好的收敛性和分布性。

To solve the problem of the lack of population diversity caused by the unrestricted replacement of neighbourhood updating strategy when the MOEA/D algorithm solves the complex optimization problem,a new MOEA/D algorithm based on new Neighbourhood Updating Strategy(MOEA/D-ENU)was proposed.In the process of evolution,the algorithm fully excavates the information of the solution,classifies the new solution generated according to the capacity of neighbourhood updating,and adopts different neighbourhood updating strategies adaptively to different types of new solutions to ensure the population convergence rate.At the same time,it takes into account the diversity of the population.The proposed algorithm was compared with five other algorithms on 9 benchmarks including ZDT,UF and CF.The values of IGD and HV show that MOEA/D-ENU has certain advantages than other algorithms in terms of convergence and distribution.