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Cloud Computing Task Scheduling Algorithm Based on Improved Particle Swarm Optimization
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Pan CHEN1, Jian SUN1, 2, *, Zhui-wei WU1, Tao WU1, Xiao-huan YANG1, Bao-quan MA1
Science Technology and Engineering | 2025, 25(12) : 5045 - 5057
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Science Technology and Engineering | 2025, 25(12): 5045-5057
Papers·Automation and Computational Technology
Cloud Computing Task Scheduling Algorithm Based on Improved Particle Swarm Optimization
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Pan CHEN1, Jian SUN1, 2, *, Zhui-wei WU1, Tao WU1, Xiao-huan YANG1, Bao-quan MA1
Affiliations
  • 1 School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
  • 2 The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
Published: 2025-04-28 doi: 10.12404/j.issn.1671-1815.2403824
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The traditional particle swarm optimization (PSO) algorithm still has shortcomings in terms of performance and efficiency of cloud computing task scheduling, such as low local search efficiency and limited search accuracy, which often makes it difficult to find the global optimal solution and easily falls into the local optimal solution. To solve this problem, an improved particle swarm optimization task scheduling algorithm(IPSO) was proposed. Firstly, a opposition-based learning strategy was used to create a more homogeneous initial population and the Rate of convergence of this algorithm was enhanced. Secondly, in the particle update process, the sine cosine algorithm(SCA) was introduced to enhance the optimization ability of the particles and balance the two processes of global search and local development. Finally, a search behavior based on average fitness was added to further expand the search solution space to find better optimal solutions and prevent falling into local optima. Experimental verification was conducted on the CloudSim simulation platform. The experimental results show that the improved particle swarm algorithm has significant advantages in reducing the cost and maximum completion time of system tasks. In particular, when the number of tasks reaches 500, IPSO improves the total cost by 10%, 4.6%, 8.6%, 9.2%, 8.2%, 10.4% and 11.3% respectively compared with adaptive particle swarm optimization (AdPSO), sine cosine algorithm-particle swarm optimization (SCA-PSO), simulated annealing particle swarm optimization (SAPSO), enhanced phagocytosis genetic algorithm (EPGA), competitive crossover mechanism genetic algorithm (C2PGA), opposition based learning-particle swarm optimization (OBL-PSO) and PSO, and improves the maximum completion time by 34.1%, 27%, 41.7%, 28.5%, 21.6%, 50.3% and 54.8% respectively, which verifies the feasibility and effectiveness of IPSO in solving cloud computing task scheduling problems under different task scales.

cloud computing  /  task scheduling  /  particle swarm optimization(PSO)  /  sine cosine algorithm(SCA)  /  CloudSim
Pan CHEN, Jian SUN, Zhui-wei WU, Tao WU, Xiao-huan YANG, Bao-quan MA. Cloud Computing Task Scheduling Algorithm Based on Improved Particle Swarm Optimization[J]. Science Technology and Engineering, 2025 , 25 (12) : 5045 -5057 . DOI: 10.12404/j.issn.1671-1815.2403824
Year 2025 volume 25 Issue 12
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Article Info
doi: 10.12404/j.issn.1671-1815.2403824
  • Receive Date:2024-05-23
  • Online Date:2025-07-09
  • Published:2025-04-28
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  • Received:2024-05-23
  • Revised:2025-01-30
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    1 School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
    2 The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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