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Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/1145
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dc.contributor.authorJ, Rejula Mercy-
dc.contributor.authorS, Elizabeth Amudhini Stephen-
dc.date.accessioned2020-08-26T06:02:14Z-
dc.date.available2020-08-26T06:02:14Z-
dc.date.issued2018-11-
dc.identifier.issnPrint:0976-6340-
dc.identifier.issnOnline:0976-6359-
dc.identifier.urihttp://www.iaeme.com/MasterAdmin/Journal_uploads/IJMET/VOLUME_9_ISSUE_11/IJMET_09_11_025.pdf-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/1145-
dc.description.abstractThe objective functions used in Engineering Optimization are complex in nature with many variables and constraints. Conventional optimization tools sometimes fail to give global optima point. Very popular methods like Genetic Algorithm, Pattern Search, Simulated Annealing, and Gradient Search are useful methods to find global optima related to engineering problems. This paper attempts to review new nontraditional optimization algorithms which are used to solve such complicated engineering problems to obtain global optimum solutionsen_US
dc.language.isoenen_US
dc.publisherInternational Journal of Mechanical Engineering & Technology (IJMET)en_US
dc.subjectAnt Lion Optimizeren_US
dc.subjectGrey Wolf Optimizeren_US
dc.subjectDragonfly Optimization Algorithmen_US
dc.subjectFirefly Algorithmen_US
dc.subjectFlower Pollination Algorithmen_US
dc.subjectWhale Optimization Algorithmen_US
dc.subjectCat Swarm Optimizationen_US
dc.subjectBat Algorithmen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectGravitational Search Algorithmen_US
dc.titleA CRITICAL STUDY ON TEN NON-TRADITIONAL OPTIMIZATION METHODS IN SOLVING ENGINEERING PROBLEMSen_US
dc.typeArticleen_US
Appears in Collections:International Journals

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