Sojoudi, Mohsen, Tavakoli, Ahmad, Pooya, Alireza, Norouzi, Mehdi. (1400). Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system. هنرهای کاربردی, 12(2), 547-561. doi: 10.22075/ijnaa.2021.23201.2491
Mohsen Sojoudi; Ahmad Tavakoli; Alireza Pooya; Mehdi Norouzi. "Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system". هنرهای کاربردی, 12, 2, 1400, 547-561. doi: 10.22075/ijnaa.2021.23201.2491
Sojoudi, Mohsen, Tavakoli, Ahmad, Pooya, Alireza, Norouzi, Mehdi. (1400). 'Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system', هنرهای کاربردی, 12(2), pp. 547-561. doi: 10.22075/ijnaa.2021.23201.2491
Sojoudi, Mohsen, Tavakoli, Ahmad, Pooya, Alireza, Norouzi, Mehdi. Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system. هنرهای کاربردی, 1400; 12(2): 547-561. doi: 10.22075/ijnaa.2021.23201.2491

Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system

International Journal of Nonlinear Analysis and Applications
دوره 12، شماره 2، بهمن 2021، صفحه 547-561    اصل مقاله (373.67 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.23201.2491
نویسندگان
Mohsen Sojoudi1؛ Ahmad Tavakoli1؛ Alireza Pooya2؛ Mehdi Norouzi3
1Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashad, Iran.
2Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashad, Iran
3Department of Medical Sciences, Tehran University, Tehran-Iran
چکیده
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.
کلیدواژه‌ها
Multi objective particles swarm؛ Cloud computing system؛ tasks timing؛ NSGA II
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