Earticle

초록

영어

To further enhance the distribution uniformity and extensiveness of the solution sets and to ensure effective convergence of the solution sets to the Pareto front, we proposed a MOEA approach based on a clustering mechanism. We named this approach improved multi-objective evolutionary algorithm (LMOEA). This algorithm uses a clustering technology to compute and maintain the distribution and diversity of the solution sets. A fuzzy C-means clustering algorithm is used for clustering individuals. Finally, the LMOEA is applied to solve the classical multi-objective knapsack problems. The algorithm performance was evaluated using convergence and diversity indicators. The proposed algorithm achieved significant improvements in terms of algorithm convergence and population diversity compared with the classical NSGA-II and the MOEA/D.

목차

Abstract
 1. Introduction
 2. Key Concepts
  2.1 Multi-objective Optimization
  2.2 Classical Literature Review
 3. An Improved Multi-objective Evolutionary Algorithm Framework
  3.1 Clustering Individuals in the Population to Form Solution Clusters
  3.2 Overall Algorithm Framework
 4. Experimental Results and Analysis
 5. Conclusion
 References

키워드

저자

• Zhanguo Li [ Software Engineering School of Pingdingshan University, Pingdingshan University, Pingdingshan, Henan province, China ]
• Qiming Wang [ Computer Science and Technic Academy Department of Pingdingshan University, Pingdingshan University, Pingdingshan, Henan province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605