Artificial algae algorithm (AAA) for nonlinear global optimization

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dc.contributor.authorUymaz, Sait Ali
dc.contributor.authorTezel, Gulay
dc.contributor.authorYel, Esra
dc.date.accessioned2020-03-26T19:00:57Z
dc.date.available2020-03-26T19:00:57Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, a novel bio-inspired metaheuristic optimization algorithm called artificial algae algorithm (AAA) inspired by the living behaviors of microalgae, photosynthetic species, is introduced. The algorithm is based on evolutionary process, adaptation process and the movement of microalgae. The performance of the algorithm has been verified on various benchmark functions and a real-world design optimization problem. The CEC'05 function set was employed as benchmark functions and the test results were compared with the algorithms of Artificial Bee Colony (ABC), Bee Algorithm (BA), Differential Evolution (DE), Ant Colony Optimization for continuous domain (ACO(R)) and Harmony Search (HSpop). The pressure vessel design optimization problem, which is one of the widely used optimization problems, was used as a sample real-world design optimization problem to test the algorithm. In order to compare the results on the mentioned problem, the methods including ABC and Standard PSO (SPS02011) were used. Mean, best, standard deviation values and convergence curves were employed for the analyses of performance. Furthermore, mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), which are computed as a result of using the errors of algorithms on functions, were used for the general performance comparison. AAA produced successful and balanced results over different dimensions of the benchmark functions. It is a consistent algorithm having balanced search qualifications. Because of the contribution of adaptation and evolutionary process, semi-random selection employed while choosing the source of light in order to avoid local minima, and balancing of helical movement methods each other. Moreover, in tested real-world application AAA produced consistent results and it is a stable algorithm. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipCoordinatorship of Scientific Research Projects of Selcuk UniversitySelcuk University [13101019]en_US
dc.description.sponsorshipThis work was financially supported by the Coordinatorship of Scientific Research Projects of Selcuk University (P.N.: 13101019).en_US
dc.identifier.doi10.1016/j.asoc.2015.03.003en_US
dc.identifier.endpage171en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage153en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2015.03.003
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31863
dc.identifier.volume31en_US
dc.identifier.wosWOS:000352955600012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial algae algorithmen_US
dc.subjectBio-inspired algorithmen_US
dc.subjectMetaheuristicen_US
dc.subjectOptimizationen_US
dc.titleArtificial algae algorithm (AAA) for nonlinear global optimizationen_US
dc.typeArticleen_US

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