A Grid-Based Decomposition for Evolutionary Multiobjective Optimization

  • Zhiwei Mei
  • Xinye CaiEmail author
  • Zhun Fan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 682)


Decomposition based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. Commonly used decomposition approaches are originated from mathematical programming and the direct use of them may not suit MOEAs due to their population-based property. This paper proposes a grid-based decomposition MOEA (G-MOEA/D). A grid has an inherent property of reflecting the information of convergence, diversity, and neighborhood structures among the solutions, which is very suitable for population-based MOEAs. The extensive experiments are conducted to compare G-MOEA/D with other state-of-art MOEAs. The results show that G-MOEA/D is very competitive with or superior to the compared algorithms.


Evolutionary multiobjective optimization Decomposition Neighborhood 


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© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.Department of Electronic Engineering, School of EngineeringShantou UniversityShantouPeople’s Republic of China

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