Inertia Constant strategy on Mean Grey Wolf Optimizer Algorithm for Optimization functions

Mean grey wolf algorithm is a crowd based technique which mimics the leadership hierarchy of wolves are very known for their group hunting. It is very interesting approach or execute most effortless and there are several constants adjust. Performance of the algorithm depends significantly on the suitable parameter value selection strategies for fine tuning its constants. Weight has been applied on the position update mathematical equations of Mean GWO to create a balance amid the exploration and exploitation characteristics of Mean GWO. In this text, has been developed a newly inertial weight based algorithm is called Inertia Constant Mena Grey Wolf Optimizer Algorithm (ICMGWO). The efficiency of the existing method has been verify on the well-known functions during to the comparison of the algorithms. Also existing variant is compared with least number of iterations, best score, 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.