A many-objective evolutionary algorithm based on rotated grid

Date

2018-03-03

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which are defined as having more than three objectives. Therefore, a more valid selection method is proposed to balance convergence and distribution. This paper proposes an algorithm using rotary grid technology to solve MaOPs (denoted by RGridEA). The algorithm uses the rotating grid to partition the objective space. Instead of using the Pareto non-dominated sorting strategy to layer the population a novel stratified method is used to enhance convergence effectively and make use of the grid to improve distribution and uniformity. Finally, with the other seven algorithm was tested on the test function DTLZ series analysis, confirming RGridEA is effective in resolving MaOPs.

Description

Keywords

Many-objective optimization, Evolutionary algorithms, clustering, genetic algorithms, Multi-objective optimization

Citation

J. Zou, L. Fu, S. Yang, J. Zheng, G. Yu, and Y. Hu. (2018) A many-objective evolutionary algorithm based on rotated grid. Applied Soft Computing, 67, pp. 596-609

Rights

Research Institute

Institute of Artificial Intelligence (IAI)