An improved two archive algorithm for many-objective optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Bingdong Li
  • Jinlong Li
  • Ke Tang
  • Xin Yao

Colleges, School and Institutes

External organisations

  • University of Science and Technology of China

Abstract

Multi-Objective Evolutionary Algorithms have been deeply studied in the research community and widely used in the real-world applications. However, the performance of traditional Pareto-based MOEAs, such as NSGA-II and SPEA2, may deteriorate when tackling Many-Objective Problems, which refer to the problems with at least four objectives. The main cause for the degradation lies in that the high-proportional non-dominated solutions severely weaken the differentiation ability of Pareto-dominance. This may lead to stagnation. The Two Archive Algorithm (TAA) uses two archives, namely Convergence Archive (CA) and Diversity Archive (DA) as non-dominated solution repositories, focusing on convergence and diversity respectively. However, as the objective dimension increases, the size of CA increases enormously, leaving little space for DA. Besides, the update rate of CA is quite low, which causes severe problems for TAA to drive forth. Moreover, since TAA prefers DA members that are far away from CA, DA might drag the population backwards. In order to deal with these weaknesses, this paper proposes an improved version of TAA, namely ITAA. Compared to TAA, ITAA incorporates a ranking mechanism for updating CA which enables truncating CA while CA overflows. Besides, a shifted density estimation technique is embedded to replace the old ranking method in DA. The efficiency of ITAA is demonstrated by the experimental studies on benchmark problems with up to 20 objectives.

Details

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publication statusPublished - 16 Sep 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • archive method, evolutionary algorithm, Many-objective, Multi-objective