Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4305
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dc.contributor.authorAditya, Nikhil-
dc.contributor.authorMahapatra, Siba Sankar-
dc.date.accessioned2024-01-12T11:17:57Z-
dc.date.available2024-01-12T11:17:57Z-
dc.date.issued2023-12-
dc.identifier.citation10th International Conference on Business Analytics and Intelligence(ICBAI), IISc Bangalore, India, 18-20 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4305-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractMetaheuristics are advanced optimization techniques used in various real-world applications for achieving the best value for a particular cost function. The search landscapes of most real-world problems are restricted due to constraints of both equality and inequality types. Since metaheuristic approaches are generalized in nature, assisting them with a constraint-handling mechanism can optimize the constrained problems. The gravitational search algorithm (GSA) is a well-known physics-based algorithm used to optimize several benchmark and real-world problems. The present study assists GSA with feasibility rules to find the optimum solution in a constrained landscape. To obtain enhanced performance than GSA, feasibility rules are coupled with chaotic GSA (CGSA). Ten constrained problems related to mechanical, civil, and chemical engineering are solved. Mean and standard deviation values justify that feasibility rules with CGSA give better results. A statistical test (Wilcoxon’s signed rank test) confirms the superior performance of CGSA.en_US
dc.subjectFeasibility rulesen_US
dc.subjectGSAen_US
dc.subjectCGSAen_US
dc.subjectconstrained landscapeen_US
dc.titleFeasibility Rules Assisted Gravitational Search Algorithm for Optimization of Design Engineering Problemsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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