Paper Info
Title
A Gravitational Search Algorithm With Chaotic Neural Oscillators
Abstract
Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2971505
IEEE ACCESS
Keywords
DocType
Volume
Chaotic neural oscillator,chaotic state,gravitational constant,gravitational search algorithm
Journal
8
ISSN
Citations 
PageRank 
2169-3536
3
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Yirui Wang11579.66
Shangce Gao248645.41
Yang Yu39455.24
Ziqian Wang471.08
Jiujun Cheng516610.39
Yuki Todo612716.95