Performance Evaluation of GWO Algorithm Using Different Initialization Strategies
P. D. Dewangan, Naresh Patnana, Lalbihari Barik, P. J. Krishna, Veerpratap Meena, Vinay Pratap Singh · 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) · 2022
This paper compares the performance of grey wolf optimization (GWO) algorithm for two different initialization strategies. These initialization strategies are adopted when a dimension of any solution over whole iterative procedure violates the search space. In first strategy, the violating dimension is initialized at the boundary of search space. While, in second one, the violating dimension is initialized randomly within the search space. Thus modified GWO algorithm is applied on five different unimodel benchmarks functions for performance comparison. The performance comparison is done on the basis of results obtained for the value of objective function (i.e. figure of merit), standard deviation, mean, minimum and maximum values figure of merit.