Paper Info
Title
A population-based hybrid extremal optimization algorithm
Abstract
The extremal optimization (EO) algorithm is a kind of evolutionary algorithm which has been applied successfully in combinatorial optimization, while its application on continuous optimization encounters the problems of heavy complexity and weak exploration ability. This paper proposes a new hybrid population-based EO algorithm, named as the adaptive co-evolution population-based extremal optimization (ACPEO) algorithm, in which all individuals co-evolve adaptively with each other and the differential evolution (DE) operator is incorporated to improve the global search ability. By employing a novel evaluation method of variables, the ACPEO algorithm performs well on several kind of benchmark problems. Experimental results show that the ACPEO algorithm is robust due to the capability for solving different problems with the same parameter setting, and it is also stable because changes in the parameters' values do not influence its performances seriously.
Year
DOI
Venue
2011
10.1007/978-3-642-24553-4_54
ICIC
Keywords
Field
DocType
eo algorithm,population-based hybrid extremal optimization,extremal optimization,acpeo algorithm,global search ability,evolutionary algorithm,benchmark problem,combinatorial optimization,adaptive co-evolution,weak exploration ability,continuous optimization,memetic algorithm
Extremal optimization,Evolutionary algorithm,Computer science,Artificial intelligence,Population-based incremental learning,Imperialist competitive algorithm,Continuous optimization,Mathematical optimization,Meta-optimization,Algorithm,Multi-swarm optimization,Bees algorithm,Machine learning
Conference
Volume
ISSN
Citations 
6840
0302-9743
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Yu Chen151749.61
Kai Zhang201.01
Xiufen Zou327225.44