| 马学森,谈 杰,陈树友,储昭坤,石 雷.云计算多目标任务调度的优化粒子群算法研究[J].电子测量与仪器学报,2020,34(8):133-143 |
| 云计算多目标任务调度的优化粒子群算法研究 |
| Research on optimal particle swarm optimization for multi-objective task scheduling in cloud computing |
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| DOI： |
| 中文关键词: 多目标任务调度 粒子群优化 自适应惯性权重 全局搜索 局部寻优 位置扰动 |
| 英文关键词:multi-objective task scheduling particle swarm optimization adaptive inertial weighting global search local optimization Elite solution |
| 基金项目:广东省科技发展专项基金(2017A010101001)、中央高校基本科研业务费专项基金(PA2019GDKPK0079)、国家留学基金、安徽省教育厅高等学校省级质量工程项目(2017JYXM0055,2019MOOC020)资助 |
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| Author | Institution |
| Ma Xuesen | 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 |
| Tan Jie | 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 |
| Chen Shuyou | 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 |
| Chu Zhaokun | 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 |
| Shi Lei | 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 |
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| 中文摘要: |
| 针对传统粒子群算法求解云计算多目标任务调度的收敛速度慢、精度低的缺陷,提出一种优化多目标任务调度粒子群
算法(MOTS-PSO)。 首先,引入非线性自适应惯性权重,改变粒子的寻优能力,避免算法陷入局部最优;其次引入花朵授粉算法
概率更新机制,平衡粒子的全局搜索和局部寻优,并对粒子的全局搜索位置更新公式进行改进;最后引入萤火虫算法,产生“精
英解”对局部搜索位置更新公式进行改进;同时利用“精英解”对粒子的位置进行扰动,跳出局部最优状态。 实验表明,MOTSPSO 算法在收敛速度和收敛精度上,比 PSO 算法提高了 27. 1%、19. 9%,比 FA 算法提高了 22. 09%、5. 2%。 进一步实验表明,
MOTS-PSO 算法在解决不同规模数量的任务调度时,比 PSO、FA 算法效果更优。 |
| 英文摘要: |
| 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. |
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