基于优化DPSO算法的云平台任务调度研究
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摘要
为提升离散粒子群优化算法(discrete PSO,DPSO)的全局收敛性和收敛效率,提出一种基于适应值的分段自适应惯性权重.根据粒子在空间搜索过程中适应度值的大小,将粒子的搜索性能分为4个状态区,粒子处于不同的状态区,拥有不同的惯性权重值.当粒子当前的适应值接近粒子群中最优粒子的适应值时,应赋予粒子较小的惯性权重值,反之,应赋予粒子较大的惯性权重值.通过动态调整粒子所处各个阶段的搜索状态,来加速粒子向全局最优解收敛.提升DPSO算法的全局搜索性能,并将优化的DPSO算法应用于云平台的任务调度.仿真实验表明,优化后的DPSO算法具有高效的全局搜索性能,能快速地为云平台提供最佳任务调度策略.
In order to improve the global convergence and convergence efficiency of the DPSO algorithm,a piecewise adaptive inertia weight was proposed based on the adaptive value in this paper.According to the size of the particle in the space search process,the search performance of the particle was divided into four state regions and in different state area the particles had different inertia weight value.When the current adaptive value of the particle was close to the adaptive value of the best particle in the particle swarm,the particle would be given a smaller inertia weight value.Otherwise,the bigger inertia weight value would be given to the particle.By dynamically adjusting the searching state of particles at all stages,the particle could be accelerated to converge to the global optimal solution.We improved the global search performance of DPSO algorithm and applied the optimized DPSO algorithm to task scheduling under cloud platform.Simulation results showed that the optimized DPSO algorithm had high efficiency in global search and provided optimal scheduling strategy for cloud platform quickly.
In order to improve the global convergence and convergence efficiency of the DPSO algorithm,a piecewise adaptive inertia weight was proposed based on the adaptive value in this paper.According to the size of the particle in the space search process,the search performance of the particle was divided into four state regions and in different state area the particles had different inertia weight value.When the current adaptive value of the particle was close to the adaptive value of the best particle in the particle swarm,the particle would be given a smaller inertia weight value.Otherwise,the bigger inertia weight value would be given to the particle.By dynamically adjusting the searching state of particles at all stages,the particle could be accelerated to converge to the global optimal solution.We improved the global search performance of DPSO algorithm and applied the optimized DPSO algorithm to task scheduling under cloud platform.Simulation results showed that the optimized DPSO algorithm had high efficiency in global search and provided optimal scheduling strategy for cloud platform quickly.
引文
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[2] 张科强.基于改进粒子群算法的矿山运输调度系统优化 [J].内蒙古师范大学学报:自然科学汉文版,2017,46(2):251-256.
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[10] Kousalya A,Radhakrishnan R.Hybrid algorithm based on genetic algorithm and PSO for task scheduling in cloud computing environment [J].International Journal of Networking and Virtual Organisations,2017,17(2-3):149-157.
[11] 黄彩娟,刘卓华,郑荣茂,等.基于改进型PSO的成本最小化云任务调度方案 [J].计算机工程与设计,2017,38(12):3349-3353+3401.
[12] 徐华,张庭.云计算环境下基于改进离散粒子群的并行调度算法 [J].华南理工大学学报:自然科学版,2015,43(9):95-99.
[13] 谢辅雯,张敏.基于改进型离散粒子群优化的云计算资源分配方案 [J].湘潭大学自然科学学报,2017,39(3):89-93.
[14] 温聪源,徐守萍,曾致远.云计算环境下基于ABC-QPSO算法的资源调度模型 [J].计算机应用与软件,2015,32(5):30-32+64.
[2] 张科强.基于改进粒子群算法的矿山运输调度系统优化 [J].内蒙古师范大学学报:自然科学汉文版,2017,46(2):251-256.
[3] 李佳.云系统负载均衡问题的优化求解 [J].内蒙古师范大学学报:自然科学汉文版,2015,44(2):184-187.
[4] Abulkhair M F,Alkayal E S,Jennings N R.Automated negotiation using parallel particle swarm optimization for cloud computing applications [C]// Computer and Applications (ICCA),2017 International Conference on.IEEE,2017:26-35.
[5] Ngatman M F,Sharif J M,Ngadi M A.A study on modified PSO algorithm in cloud computing [C]// Student Project Conference (ICT-ISPC),2017 6th ICT International.IEEE,2017:1-4.
[6] Kumari K R,Sengottuvelan P,Shanthini J.A hybrid approach of genetic algorithm and multi objective PSO task scheduling in cloud computing [J].Asian Journal of Research in Social Sciences and Humanities,2017,7(3):1260-1271.
[7] 袁正午,李君琪.基于改进粒子群算法的云资源调度 [J].计算机工程与设计,2016,37(2):401-404+412.
[8] 邬开俊,鲁怀伟.云环境下基于DPSO的任务调度算法 [J].计算机工程,2014,40(1):59-62.
[9] Ke M T,Yeh C H,Su C J.Cloud computing platform for real-time measurement and verification of energy performance [J].Applied Energy,2017,188:497-507.
[10] Kousalya A,Radhakrishnan R.Hybrid algorithm based on genetic algorithm and PSO for task scheduling in cloud computing environment [J].International Journal of Networking and Virtual Organisations,2017,17(2-3):149-157.
[11] 黄彩娟,刘卓华,郑荣茂,等.基于改进型PSO的成本最小化云任务调度方案 [J].计算机工程与设计,2017,38(12):3349-3353+3401.
[12] 徐华,张庭.云计算环境下基于改进离散粒子群的并行调度算法 [J].华南理工大学学报:自然科学版,2015,43(9):95-99.
[13] 谢辅雯,张敏.基于改进型离散粒子群优化的云计算资源分配方案 [J].湘潭大学自然科学学报,2017,39(3):89-93.
[14] 温聪源,徐守萍,曾致远.云计算环境下基于ABC-QPSO算法的资源调度模型 [J].计算机应用与软件,2015,32(5):30-32+64.