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- W2791692320 abstract "Mean Grey wolf optimizer is a typical population based nature inspired technique which mimics the leadership hierarchy of wolves are well known for their group hunting. It is most effortless to execute and there are several constant adjust. The accuracy of mean grey optimizer variant depends significantly on the suitable constant selection strategies for fine tuning its constants. Inertia constant is one of mean grey optimizer algorithm constant applied to bring about a balance between the exploitation and exploration characteristics of mean grey wolf optimizer. In this article, has been presented a newly modified strategy on the basis of inertia constant which is named Inertia Constant Mean Grey Wolf Optimizer Algorithm (ICMGWO). The efficiency and efficacy of mean grey wolf optimizer variant with ICMGWO and several recent meta-heuristics is validated on a standard benchmark function and some engineering real life applications. Also existing variant is compared with least number of iterations, minimum objective function values, maximum objective function value, standard deviation, mean, convergence rate and best time varying. Statistical analysis and experimental solutions reveals that existing variant improves the search accuracy in terms of convergence rate as well as solution quality." @default.
- W2791692320 created "2018-03-29" @default.
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- W2791692320 date "2018-02-19" @default.
- W2791692320 modified "2023-09-23" @default.
- W2791692320 title "ICMGWO: A Novel Flexible Inertia Constant Mean Grey Wolf Optimizer Algorithm for Engineering Real Life Applications" @default.
- W2791692320 hasPublicationYear "2018" @default.
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