A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems.

Date

2009

Advisors

Journal Title

Journal ISSN

ISSN

1432-7643

Volume Title

Publisher

Springer-Verlag

Type

Article

Peer reviewed

Yes

Abstract

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

Description

Keywords

Genetic algorithm (GA), Memetic algorithm, Local search, Crossover-basedhill climbing, Mutation-based hill climbing, Dual mapping, Triggeredrandom immigrants, Dynamic optimization problems (DOP)

Citation

Wang, H., Wang, D. and Yang, S. (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 13(8-9), July 2009, pp. 763-780.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)