Title
Novel Binary Differential Evolution Algorithm for Discrete Optimization
Abstract
New binary differential evolution algorithm was proposed for the combinatorial optimization problem. With the same framework of the original differential evolution algorithm, three new operators were used to expand the continuous field of the original differential evolution to the discrete field. Firstly, a new operator, mapping operator, in the new algorithm was used to ensure the original mutation operator still effective. Then a new S operator, with sigmoid function, was used to keep the result of the mutation operator falls in the interval. Before the crossover operator, an inverse mapping operator transformed the continuous numbers to discrete. Two initial simulation results show it is effective and useful.
Year
DOI
Venue
2009
10.1109/ICNC.2009.188
ICNC (4)
Keywords
Field
DocType
crossover operator,optimisation,new operator,sigmoid function,original differential evolution algorithm,evolutionary computation,discrete optimization,original differential evolution,mathematical operators,combinatorial optimization problem,mapping operator,combinatorial mathematics,binary differential evolution,inverse mapping operator,operator,original mutation operator,novel binary differential evolution,new binary differential evolution,new algorithm,binary differential evolution algorithm,mutation operator,artificial neural networks,optimization,data mining,particle swarm optimization,chromium,differential evolution
Genetic operator,Shift operator,Multiplication operator,Semi-elliptic operator,Computer science,Symbol of a differential operator,Artificial intelligence,Operator (computer programming),Hypoelliptic operator,Mathematical optimization,Pseudo-differential operator,Algorithm,Machine learning
Conference
Volume
ISBN
Citations 
4
978-0-7695-3736-8
3
PageRank 
References 
Authors
0.41
6
4
Name
Order
Citations
PageRank
Changshou Deng13910.80
Bingyan Zhao2123.88
Yanling Yang392.27
An-Yuan Deng4133.05