Paper Info
Title
Some modifications of low-dimensional simplex evolution and their convergence
Abstract
Low-dimensional simplex evolution LDSE is a real-coded evolutionary algorithm for global optimization. In this paper, we introduce three techniques to improve its performance: low-dimensional reproduction LDR, normal struggle NS and variable dimension VD. LDR tries to preserve the elite by keeping some of its randomly chosen components. LDR can also help the offspring individuals to escape from the hyperplane determined by their parents. NS tries to enhance its local search capability by allowing unlucky individual search around the best vertex of m-simplex. VD tries to draw lessons from recent failure by making further exploitation on its most promising sub-facet. Numerical results show that these techniques can improve the efficiency and reliability of LDSE considerably. The convergence properties are then analysed by finite Markov chains. It shows that the original LDSE might fail to converge, but modified LDSE with the above three techniques will converge for any initial population. To evaluate the convergence speed of modified LDSE, an estimation of its first passage time of reaching the global minimum is provided.
Year
DOI
Venue
2013
10.1080/10556788.2011.584876
Optimization Methods and Software
Keywords
Field
DocType
convergence speed,convergence property,original ldse,low-dimensional simplex evolution,best vertex,unlucky individual search,global optimization,local search capability,modified ldse,global minimum,first passage time,local search,markov chain,evolutionary algorithm,genetic algorithm
Convergence (routing),Population,Mathematical optimization,Global optimization,Evolutionary algorithm,Markov chain,Algorithm,Hyperplane,Local search (optimization),Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
28
1
1055-6788
Citations 
PageRank 
References 
3
0.41
12
Authors
3
Name
Order
Citations
PageRank
Changtong Luo1365.66
Shao-Liang Zhang29219.06
Bo Yu3306.46