Projects per year
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.
Original language | English |
---|---|
Pages (from-to) | 924 - 938 |
Number of pages | 15 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 20 |
Issue number | 6 |
Early online date | 31 Mar 2016 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Keywords
- stochastic ranking
- Archive method
- many-objective evolutionary algorithm
- multi-indicator
- multiobjective optimization
Fingerprint
Dive into the research topics of 'Stochastic Ranking Algorithm for Many-Objective Optimization Based on Multiple Indicators'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Evolutionary Computation for Dynamic Optimisation in Network Environments
Engineering & Physical Science Research Council
25/02/13 → 17/08/17
Project: Research Councils
-
Evolutionary Approximation Algorithms for Optimisation: Algorithm Design and Complexity Analysis
Engineering & Physical Science Research Council
29/04/11 → 28/10/15
Project: Research Councils