Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators

Research output: Contribution to journalArticlepeer-review

Authors

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

Colleges, School and Institutes

External organisations

  • University of Science and Technology of China, Hefei

Abstract

Traditional multi-objective evolutionary algorithms face great challenge when dealing with many objectives. This is due to a high proportion of non-dominated solutions in the population and low selection pressure towards the Pareto front. In order to tackle this issue, a series of indicator-based algorithms have been proposed to guide the search process towards the Pareto front. However, a single indicator might be biased and lead the population to converge to a sub-region of the Pareto front. In this paper, a multi-indicator based algorithm is proposed for many-objective optimization problems. The proposed algorithm, namely Stochastic Ranking based multi-indicator Algorithm (SRA), adopts the stochastic ranking technique to balance the search biases of different indicators. Empirical studies on a large number (39 in total) of problem instances from two well-defined benchmark sets with 5, 10 and 15 objectives demonstrate that SRA performs well in terms of Inverted Generational Distance and hypervolume metrics when compared with state-of-the-art algorithms. Empirical studies also reveal that, in case a problem requires the algorithm to have strong convergence ability, the performance of SRA can be further improved by incorporating a direction-based archive to store well-converged solutions and maintain diversity.

Details

Original languageEnglish
Pages (from-to)924 - 938
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume20
Issue number6
Early online date31 Mar 2016
Publication statusPublished - 1 Dec 2016

Keywords

  • stochastic ranking, Archive method, many-objective evolutionary algorithm, multi-indicator, multiobjective optimization