Publication:
Artificial immune system based remainder method for multimodal mathematical function optimization

Simple item page
dc.citedby2
dc.contributor.authorYap D.F.W.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorid22952562500en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.date.accessioned2023-12-28T07:05:42Z
dc.date.available2023-12-28T07:05:42Z
dc.date.issued2011
dc.description.abstractArtificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hyper mutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions. � IDOSI Publications, 2011.en_US
dc.description.natureFinalen_US
dc.identifier.epage1514
dc.identifier.issue10
dc.identifier.scopus2-s2.0-84856165143
dc.identifier.spage1507
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84856165143&partnerID=40&md5=5481147bbe109b2dbe97fdbae48307a3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29581
dc.identifier.volume14
dc.pagecount7
dc.sourceScopus
dc.sourcetitleWorld Applied Sciences Journal
dc.subjectAffinity maturation
dc.subjectAntibody
dc.subjectAntigen
dc.subjectClonal selection
dc.subjectComponent
dc.subjectMutation
dc.titleArtificial immune system based remainder method for multimodal mathematical function optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS