Escape velocity: a new operator for gravitational search algorithm

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dc.contributor.authorGüvenç, Uğur
dc.contributor.authorKatırcıoğlu, Ferzan
dc.date.accessioned2020-05-01T12:11:54Z
dc.date.available2020-05-01T12:11:54Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionGUVENC, Ugur/0000-0002-5193-7990en_US
dc.descriptionWOS: 000457458000003en_US
dc.description.abstractGravitational search algorithm (GSA) is based on the feature of reciprocal acceleration tendency of objects with masses. The total force, which is formed as an influence of other agents, is an important variable in the calculation of agent velocity. It has been determined that the total force and, thus, the velocity of the agents that are located far away, is low due to the distance. In this case, they continue their search in bad areas, as their velocity is low, which means a decrease in their contribution to optimization result. In this paper, a new operator called escape velocity has been proposed which is inspired by the real nature of GSA. It has been suggested that adding the escape velocity negatively will enable the agents that remain far away or outside of group behavior to be included in the group or to be increased in velocity. Thus, the study of perfecting the herd or group approach within the search scope has been carried out. To evaluate the performance of our algorithm, we applied it to 23 standard benchmark functions and six composite test functions. Escape velocity gravitational search algorithm (EVGSA) has been compared with some well-known heuristic search algorithms such as GSA, genetic algorithm (GA), particle swarm optimization (PSO), and recently the new algorithm dragonfly algorithm (DA). Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by GSA and EVGSA. Standard and composite benchmark tables and Wilcoxon signed-rank test and visual results show that EVGSA is more powerful than other algorithms.en_US
dc.identifier.doi10.1007/s00521-017-2977-9en_US
dc.identifier.endpage42en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage27en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-017-2977-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6261
dc.identifier.volume31en_US
dc.identifier.wosWOS:000457458000003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGravitational search algorithmen_US
dc.subjectEscape velocityen_US
dc.subjectOptimization algorithmsen_US
dc.titleEscape velocity: a new operator for gravitational search algorithmen_US
dc.typeArticleen_US

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