JUCS - Journal of Universal Computer Science 23(7): 652-672, doi: 10.3217/jucs-023-07-0652
An Adaptive Membrane Evolutionary Algorithm for Solving Constrained Engineering Optimization Problems
Jianhua Xiao‡, Ying Liu‡, Shuai Zhang§, Ping Chen‡‡ Nankai University, Tianjin, China§ University of Manitoba, Winnipeg, Canada
Corresponding author: Jianhua Xiao ( jhxiao@nankai.edu.cn ) © Jianhua Xiao, Ying Liu, Shuai Zhang, Ping Chen. This article is freely available under the J.UCS Open Content License. Citation:
Xiao J, Liu Y, Zhang S, Chen P (2017) An Adaptive Membrane Evolutionary Algorithm for Solving Constrained Engineering Optimization Problems. JUCS - Journal of Universal Computer Science 23(7): 652-672. https://doi.org/10.3217/jucs-023-07-0652 |  |
Abstract
In this paper, an adaptive membrane evolutionary algorithm (AMEA) is proposed, which combines a dynamic membrane structure and a differential evolution with the adaptive mutation factor. In the AMEA, the feasibility proportion method is used to dynamically adjust the size of the elementary membrane in the optimization process. The results of the experimental indicate that the proposed algorithm outperforms other evolutionary algorithms on five well-known constrained engineering optimization problems.
Keywords
membrane computing, membrane algorithm, differential evolution, engineering optimization problem