Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/10708
DC FieldValueLanguage
dc.contributor.authorYasin, Z.M.en_US
dc.contributor.authorAziz, N.F.A.en_US
dc.contributor.authorSalim, N.A.en_US
dc.contributor.authorWahab, N.A.en_US
dc.contributor.authorRahmat, N.A.en_US
dc.date.accessioned2018-11-07T08:19:36Z-
dc.date.available2018-11-07T08:19:36Z-
dc.date.issued2018-
dc.description.abstractIn this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques. © 2018 Institute of Advanced Engineering and Science All rights reserved.-
dc.language.isoenen_US
dc.titleOptimal economic load dispatch using multiobjective cuckoo search algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.11591/ijeecs.v12.i1.pp168-174-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:CCI Scholarly Publication
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