Earticle

초록

영어

Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous Multiobjective Optimization Problems(MOPs) is a piecewise continuous (m 1)  D manifold(where m is the number of objectives). One hand, the traditional Multiobjective Optimization Algorithms(EMOAs) cannot utilize this regularity property; on the other hand, the Regular Model-Based Multiobjective Estimation of Distribution Algorithm(RM-MEDA) only able to build the linear model of decision space using linear modelling algorithm, such as: the local principal component analysis algorithm(Local PCA).Aim at the shortcomings of EMOAs and RM-MEDA, the Manifold-Learning-Based Multiobjective Evolutionary Algorithm Via Self-Organizing Maps(ML-MOEA/SOM) is proposed for continuous multiobjective optimization problems. At each generation, first, via Self-Organizing Maps, the proposed algorithm learns such a nonlinear manifold in the decision space; then, new trial solutions is built through expanding the neurons of SOM with random noise; at the end, a nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, ML-MOEA/SOM outperforms NSGA-II, and is competitive with RM-MEDA in terms of convergence and diversity, on a set of test instances with variable linkages. We have demonstrated that, compared with NSGA-II and RM-MEDA, via self-Organizing maps, ML-MOEA/SOM can dig nonlinear manifold hidden in the decision space of multiobjective optimization problems.

목차

Abstract
 1. Introduction
 2. Problem Definition
 3. The Alogorithm Framwork
  3.1. Basic Idea
  3.2.The Algorithm Framework
  3.3. Framework of ML-MOEA/SOM:
  3.4. Extend and Reproduction
 4. Experimental Studies
  4.1. Test Instances
  4.2. Performance Metric
  4.3. Experimental Setting
  4.4. Experimental Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Wei Cao [ School of computer and Information Engineering, Xiamen University of Technology, Xiamen, Fujian, China 361024; ]
• Wei Zhan [ School of Computer Science, Yangtze University, JingZhou, HuBei, China 434023 ] Corresponding author
• ZhiQiang Chen [ School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, Missouri ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605