Meta-heuristic Innovative Algorithm of Multi Objectives in Tasks Timing at Cloud Computing System

In this article a mathematical model with twin objectives is presented. The objectives are considered as: Minimization of the maximum tardiness of tasks completion time and the total early tasks penalties. Since tasks timing is a tardy and indefinite factor in cloud computing; therefore problem solving model is used as the combined Meta-heuristic innovative algorithm of multi objective swarm of particles based Parto archive has been used. The suggested algorithm with genetic operators as well as the directed and repeated counterpart structures in the format of multi operators are taken to assess the algorithm application. The results will be sorted based on quality, distraction, integrated, the number of non-defeated solutions and the gap from the ideal one is compared with the evolutionary algorithm results titled genetic algorithm. The final results of solved model indicate that firstly, this algorithm is stronger than NSGA-II algorithm but is weaker in timing, norms and scales. In other words, the suggested algorithm, is more capable to discover solutions, accordingly.