a modified variant of grey wolf optimizer

The original version of grey wolf optimization (gwo) algorithm has small number of disadvantages of low solving accuracy, bad local searching ability and slow convergence rate. in order to overcome these disadvantages of grey wolf optimizer, a new version of grey wolf optimizer algorithm has been proposed by modifying the encircling behavior and position update equations of grey wolf optimization algorithm. the accuracy and convergence performance of modified variant is tested on several well known classical further more like sine dataset and cantilever beam design functions. for verification, the results are compared with some of the most powerful well known algorithms i.e. particle swarm optimization, grey wolf optimizer and mean grey wolf optimization. the experimental solutions demonstrate that the modified variant is able to provide very competitive solutions in terms of improved minimum objective function value, maximum objective function value, mean, standard deviation and convergence rate.