caa a22 4500 605468869 CHVBK 20210128100319.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1500-9 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1500-9 Artificial bee and differential evolution improved by clustering search on continuous domain optimization [Elektronische Daten] [Tarcísio Costa, Alexandre de Oliveira] 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. Springer-Verlag Berlin Heidelberg, 2014 Clustering Search nationallicence Artificial Bee Colony nationallicence Promising areas nationallicence Hybrid approaches nationallicence Costa Tarcísio Federal Institute of Maranhão, Pacas Street 5, Pinheiro Campus, Pinheiro, Brazil aut de Oliveira Alexandre Federal University of Maranhão, Portugueses Avenue, No Number, Bacanga Campus, São Luís, Brazil aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2457-2468 1432-7643 19:9<2457 2015 19 500 https://doi.org/10.1007/s00500-014-1500-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1500-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Costa Tarcísio Federal Institute of Maranhão, Pacas Street 5, Pinheiro Campus, Pinheiro, Brazil aut NATIONALLICENCE 700 1- de Oliveira Alexandre Federal University of Maranhão, Portugueses Avenue, No Number, Bacanga Campus, São Luís, Brazil aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2457-2468 1432-7643 19:9<2457 2015 19 500