Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (5): 191-197.doi: 10.11896/j.issn.1002-137X.2019.05.029

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MOEA/D Algorithm Based on New Neighborhood Updating Strategy

GENG Huan-tong, HAN Wei-min, ZHOU Shan-sheng, DING Yang-yang   

  1. (College of Computer & Software,Nanjing University of Information Science&Technology,Nanjing 210044,China)
  • Received:2018-04-04 Revised:2018-07-27 Published:2019-05-15

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Abstract: 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.

Key words: Classification, Excavating solution, Multiobjective evolutionary algorithm based on decomposition, Neighbourhood updating strategy

CLC Number: 

  • TP183
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