An Improved Grey Wolves Optimization Algorithm For Workflow Scheduling In Cloud Computing Environment

In this paper, An improved meta-heuristic algorithm are proposed based on the meta-heuristic grey wolf algorithm for solving optimization problems. In proposed algorithm, we remove the weakest wolves from the population and put them in with the wolves of the initial population. Wolves selecting can be randomly or on a fitness basis. In this algorithm, the particle positioning accuracy is checked for each repetition, and if the wolf's fitness is improved, they will move towards the target, otherwise they will remain in the last state. This algorithm is designed to improve search performance in solving various issues, increase the rate of convergence and avoid local optimal. Simulation in Matlab software has been implemented on 23 different mathematical optimization functions. By comparing the performance and statistical comparison of the results obtained from the new algorithm with the basic grey wolves algorithm and several other algorithms, we conclude that by proper adjustment of the parameters, the improvements made have a significant effect on the function of the algorithm on different functions.