Paper Info
Title
A small world algorithm for high-dimensional function optimization
Abstract
In this paper, we describe a new small world optimization algorithm for obtaining satisfactory solution for high-dimensional function. Based on the small world phenomenon which is revealed in Milgram's sociological experiment, some operators with decimal-coding strategy are proposed, and then an "imitated society" decimal-coding small world optimization algorithm (DSWOA) is designed to solve high-dimensional function optimization. Compared with the corresponding evolution algorithms, such as orthogonal genetic algorithm with quantization (OGA/Q), the simulation results of several benchmark functions with high dimension show that DSWOA can acquire satisfied solution, has also a better stability, and a fast convergence rate. Therefore, it is feasible to solve high-dimensional optimization problems.
Year
DOI
Venue
2009
10.1109/CIRA.2009.5423233
CIRA
Keywords
DocType
Volume
genetic algorithm,convergence rate,optimization problem,satisfiability
Conference
null
Issue
Citations 
PageRank 
null
1
0.36
References 
Authors
1
5
Name
Order
Citations
PageRank
Xiaohu Li121.06
Jinhua Zhang2136.40
Sunan Wang33810.17
Mao-Lin Li492.24
Kunpeng Li5516.37