Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm

dc.contributor.authorAlawode, Kehinde
dc.contributor.authorKomolafe, Olusola
dc.contributor.authorAbimbola, Jubril
dc.date.accessioned2023-05-13T16:14:54Z
dc.date.available2023-05-13T16:14:54Z
dc.date.issued2010-01
dc.descriptionInternational journal of electrical and electric engineering,Vol.4, Page No.506_511.en_US
dc.description.abstractThis paper solves the environmental/ economic dispatch power system problem using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator Operator (CAO), called the NSGA-II/CAO. These multiobjective evolutionary algorithms were applied to the standard IEEE 30-bus six-generator test system. Several optimization runs were carried out on different cases of problem complexity. Different quality measure which compare the performance of the two solution techniques were considered. The results demonstrated that the inclusion of the CAO in the original NSGA-II improves its convergence while preserving the diversity properties of the solution set.en_US
dc.identifier.urihttps://ir.oauife.edu.ng/123456789/5190
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjecteconomic dispatchen_US
dc.subjectConvergence Accelerator Operatoren_US
dc.subjectNon-dominated Sorting Genetic Algorithm-IIen_US
dc.subjectmultiobjective evolutionary algorithmsen_US
dc.titleMultiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithmen_US
dc.typeJournalen_US
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