Paper Info
Title
Artificial bee and differential evolution improved by clustering search on continuous domain optimization.
Abstract
Clustering Search (*CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated with different search subspaces. In this paper, new approaches are proposed, based on *CS, as an Artificial Bee Colony-based one, which detects promising food sources alike *CS approaches. The other new *CS approach is based on Differential Evolution (DE) algorithm. The DE is just a CS component (the evolutionary algorithm), different from ABC-based approach, called Artificial Bee Clustering Search (ABCS). ABCS tries to find promising solutions using some concepts from CS. The proposed hybrid algorithms, performing a Hooke and Jeeves-based local, are compared to another hybrid approaches, exploring an elitist criteria to apply local search. The experiments show that the proposed ABCS and Differential Evolutionary Clustering Search (DECS) are competitive for the majority continuous optimization functions benckmarks selected in this paper.
Year
DOI
Venue
2015
10.1007/s00500-014-1500-9
soft computing
Keywords
Field
DocType
Clustering Search, Artificial Bee Colony, Promising areas, Hybrid approaches
Continuous optimization,Mathematical optimization,Evolutionary algorithm,Computer science,Linear subspace,Differential evolution,Evolutionary clustering,Artificial intelligence,Local search (optimization),Cluster analysis,Machine learning,Metaheuristic
Journal
Volume
Issue
ISSN
19
9
1433-7479
Citations 
PageRank 
References 
1
0.36
13
Authors
2
Name
Order
Citations
PageRank
Tarcísio Souza Costa1172.24
Alexandre César Muniz De Oliveira2838.30