Behavior Analysis of the New PSO-CGSA Algorithm in Solving the Combined Economic Emission Dispatch Using Non-parametric Tests

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dc.citation.issue1
dc.citation.rankM22
dc.citation.spage2322335
dc.citation.volume38
dc.contributor.authorGajić, Milena
dc.contributor.authorArsić, Sanela
dc.contributor.authorRadosavljević, Jordan
dc.contributor.authorJevtić, Miroljub
dc.contributor.authorPerović, Bojan
dc.contributor.authorKlimenta, Dardan
dc.contributor.authorMilovanović, Miloš
dc.date.accessioned2024-10-08T08:04:08Z
dc.date.available2024-10-08T08:04:08Z
dc.date.issued2024
dc.description.abstractThis paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. First, we determine the efficiency and effectiveness measures of the algorithm and compare it with other well-known algorithms. Then, we analyze the obtained solutions using the statistical procedure proposed in the paper. The proposed procedure contains the following: (i) the behavior analysis of the algorithms when solving the CEED problem, using non-parametric tests, and (ii) the ranking of the algorithms using the PROMETHEE/GAIA multi-criteria decisionmaking method. The behavior analysis is performed for two cases: (i) when solving individual variants of the CEED problem (single-problem analysis) and (ii) when solving a set of CEED variants (multiple-problem analysis). The results of the applied procedure for the test system with six generators show that PSO-CGSA has (i) the best solution for each tested variant of the CEED problem; (ii) the best standard deviation, mean value, error rate, and behavior for the CEED variant with a bi-objective function that simultaneously minimizes fuel cost and emission, taking into account the valve point effect; and (iii) the best rank when solving a set of CEED variants.
dc.identifier.doi10.1080/08839514.2024.2322335
dc.identifier.issn1087-6545
dc.identifier.issn0883-9514
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5863
dc.language.isoen
dc.publisherTaylor & Francis Group
dc.rights.licenseARR
dc.rights.uriAll rights reserved
dc.sourceApplied Artificial Intelligence
dc.subjectcombined economic emission dispatch (CEED)
dc.subjectmetaheuristics
dc.subjectmulti-criteria decision making (MCDM)
dc.subjectnon-parametric tests
dc.subjectparticle swarm optimization
dc.titleBehavior Analysis of the New PSO-CGSA Algorithm in Solving the Combined Economic Emission Dispatch Using Non-parametric Tests
dc.typearticle
dc.type.versionpublishedVersion

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