A dominance tree and its application in evolutionary multi-objective optimization

  • Authors:

  • Chuan Shi;Zhenyu Yan;Kevin Lü;Zhongzhi Shi;Bai Wang

  • Affiliations:

  • Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, China;Department of Systems and Information Engineering, University of Virginia, USA;Brunel University, Uxbridge, UB8 3PH, UK;Institute of Computing Technology, Chinese Academy of Sciences, China;Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

Quantified Score

Hi-index 0.07

Visualization

Abstract

Most contemporary multi-objective evolutionary algorithms (MOEAs) store and handle a population with a linear list, and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes. This paper presents a data structure for storing the whole population and their dominating information in MOEAs. This structure, called a Dominance Tree (DT), is a binary tree that can effectively and efficiently store three-valued relations (namely dominating, dominated or non-dominated) among vector values. This paper further demonstrates DT's potential applications in evolutionary multi-objective optimization with two cases. The first case utilizes the DT to improve NSGA-II as a fitness assignment strategy. The second case demonstrates a DT-based MOEA (called a DTEA), which is designed by leveraging the favorable properties of the DT. The simulation results show that the DT-improved NSGA-II is significantly faster than NSGA-II. Meanwhile, DTEA is much faster than SPEA2, NSGA-II and an improved version of NSGA-II. On the other hand, in regard to converging to the Pareto optimal front and maintaining the diversity of solutions, DT-improved NSGA-II and DTEA are found to be competitive with NSGA-II and SPEA2.