A steady-state and generational evolutionary algorithm for dynamic multi-objective optimization

Date

2016-05-10

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Press

Type

Article

Peer reviewed

Yes

Abstract

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environmental changes and responds to them in a steady-state manner. If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous environments and the new environment. This way, the algorithm can quickly adapt to changing environments and thus is expected to provide a good tracking ability. The proposed algorithm is tested on a number of bi- and three-objective benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.

Description

Keywords

Citation

Jiang, S. and Yang, S. (2017) A steady-state and generational evolutionary algorithm for dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation, 21 (1), pp. 65-82

Rights

Research Institute

Institute of Artificial Intelligence (IAI)