Achieving balance between proximity and diversity in multi-objective evolutionary algorithm

  • Authors:

  • Ke Li;Sam Kwong;Jingjing Cao;Miqing Li;Jinhua Zheng;Ruimin Shen

  • Affiliations:

  • Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;College of Information and Engineering, Xiangtan University, China;College of Information and Engineering, Xiangtan University, China;College of Information and Engineering, Xiangtan University, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

Quantified Score

Hi-index 0.07

Visualization

Abstract

Currently, an alternative framework using the hypervolume indicator to guide the search for elite solutions of a multi-objective problem is studied in the evolutionary multi-objective optimization community very actively, comparing to the traditional Pareto dominance based approach. In this paper, we present a dynamic neighborhood multi-objective evolutionary algorithm based on hypervolume indicator (DNMOEA/HI), which benefits from both Pareto dominance and hypervolume indicator based frameworks. DNMOEA/HI is featured by the employment of hypervolume indicator as a truncation operator to prune the exceeded population, while a well-designed density estimator (i.e., tree neighborhood density) is combined with the Pareto strength value to perform fitness assignment. Moreover, a novel algorithm is proposed to directly evaluate the hypervolume contribution of a single individual. The performance of DNMOEA/HI is verified on a comprehensive benchmark suite, in comparison with six other multi-objective evolutionary algorithms. Experimental results demonstrate the efficiency of our proposed algorithm. Solutions obtained by DNMOEA/HI well approach the Pareto optimal front and are evenly distributed over the front, simultaneously.