Task scheduling algorithm based on fireworks algorithm

被引：8

作者：

Li, Jingmei ^{[1
]}

Tian, Qiao ^{[1
]}

Zhang, Guoyin ^{[1
]}

Wu, Weifei ^{[1
]}

Xue, Di ^{[1
]}

Li, Lanting ^{[1
]}

Wang, Jiaxiang ^{[1
]}

Chen, Lei ^{[2
]}

机构：

[1] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China

[2] Georgia Southern Univ, Statesboro, GA USA

来源：

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING | 2018年

关键词：

Task scheduling; Fireworks algorithm; Gaussian mutation; Adaptive; GENETIC ALGORITHM; DUPLICATION; OPTIMIZATION; PERFORMANCE; NETWORKS; GRAPHS; NUMBER;

D O I：

10.1186/s13638-018-1259-2

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.

引用

页数：8

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