1. School of Computer and Information, Hefei University of Technology,2. Research Institute of Sanshui & Hefei University of Technology in Guangdong,3. Engineering Research Center of Safety Critical Industiral Measurement and Control Technology,Ministy of Education 在期刊界中查找 在百度中查找 在本站中查找
1. School of Computer and Information, Hefei University of Technology,3. Engineering Research Center of Safety Critical Industiral Measurement and Control Technology,Ministy of Education 在期刊界中查找 在百度中查找 在本站中查找
1. School of Computer and Information, Hefei University of Technology,3. Engineering Research Center of Safety Critical Industiral Measurement and Control Technology,Ministy of Education 在期刊界中查找 在百度中查找 在本站中查找
1. School of Computer and Information, Hefei University of Technology,3. Engineering Research Center of Safety Critical Industiral Measurement and Control Technology,Ministy of Education 在期刊界中查找 在百度中查找 在本站中查找
1. School of Computer and Information, Hefei University of Technology,3. Engineering Research Center of Safety Critical Industiral Measurement and Control Technology,Ministy of Education 在期刊界中查找 在百度中查找 在本站中查找
To overcome the slow convergence and low accuracy of traditional particle swarm optimization (PSO) for slow convergence and low accuracy of multi-objective task scheduling in cloud computing, an optimized multi-objective task scheduling particle swarm optimization algorithm (MOTS-PSO) is proposed. Firstly, the nonlinear adaptive inertial weight is introduced to change the particle’ s optimization ability to avoid the algorithm from running into local optimum. Secondly, the flower pollination algorithm probability update mechanism is introduced to balance the global search and local optimization of the particles. In addition, we improve the global search position update formula. Finally, the firefly algorithm ( FA) is introduced to generate the elite solution to improve the local search position update formula. At the same time, we utilize the elite solution to perturb the particle position and to jump out of the local optimal state. Experiments show that the MOTS-PSO algorithm has 27. 1% and 19. 9% higher convergence speed and precision than the PSO algorithm, and 22. 09% and 5. 2% higher than the FA algorithm. Further experiments show that the MOTS-PSO algorithm is more effective than the PSO and FA algorithms in solving tasks of different sizes and numbers.
