Paper Info
Title
Immune gravitation inspired optimization algorithm
Abstract
The traditional Gravitational Search Algorithm (GSA) has the advantages of easy implementation, fast convergence and low computational cost. However, GSA driven by the gravity law is easy to fall into local optimum solution. The convergence speed slows down in the later search stage, and the solution precision is not good. Inspired by the biological immune system, we introduce the characteristics of antibody diversity and vaccination, and propose a novel immune gravitation optimization algorithm (IGOA) to help speed the convergence of evolutionary algorithms and improve the optimization capability. The comparison experiments of IGOA, GSA and PSO on some benchmark functions are carried out. The proposed algorithm shows competitive results with improved diversity and convergence. It provides new opportunities for solving previously intractable function optimization problems.
Year
DOI
Venue
2011
10.1007/978-3-642-24728-6_24
ICIC (1)
Keywords
DocType
Volume
convergence speed,antibody diversity,optimization algorithm,easy implementation,biological immune system,intractable function optimization problem,optimization capability,evolutionary algorithm,improved diversity,novel immune gravitation optimization,fast convergence
Conference
6838
ISSN
Citations 
PageRank 
0302-9743
5
0.48
References 
Authors
5
4
Name
Order
Citations
PageRank
Yu Zhang1112.73
Lihua Wu2142.50
Ying Zhang316325.25
Jianxin Wang42163283.94