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

Authors:
Hongfeng Wang;Dingwei Wang;Shengxiang Yang
Affiliations:
Northeastern University, School of Information Science and Engineering, 110004, Shenyang, People’s Republic of China;Northeastern University, School of Information Science and Engineering, 110004, Shenyang, People’s Republic of China;University of Leicester, Department of Computer Science, University Road, LE1 7RH, Leicester, UK
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Year:
2009

Citing 0
Cited 7

An unorthodox introduction to Memetic Algorithms

ACM SIGEVOlution
A particle swarm optimization based memetic algorithm for dynamic optimization problems

Natural Computing: an international journal
A multiple local search algorithm for continuous dynamic optimization

Journal of Heuristics
Comprehensive Survey of the Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation
Power law-based local search in differential evolution

International Journal of Computational Intelligence Studies
An optimization algorithm employing multiple metamodels and optimizers

International Journal of Automation and Computing
Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

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.