Abstract
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.
Highlights
The task scheduling problem belongs to the combinatorial optimization problem and cannot be solved within the polynomial time complexity [1]
5.1 Experimental methods For evaluating the performance of Improved fireworks algorithm (IMFWA) algorithm, this research designed the comparative experiments with the basic fireworks algorithm and genetic algorithm which is widely used in task scheduling
It can be seen that the accuracy of IMFWA and FWA is superior to that of the genetic algorithm
Summary
The task scheduling problem belongs to the combinatorial optimization problem and cannot be solved within the polynomial time complexity [1]. It has been proved to be an NP-hard problem [2, 3] It is easy for multiple independent tasks to be scheduled on homogeneous multiprocessors, by only scheduling the task with the shortest completion time to the processor. The representative task scheduling scheme on heterogeneous multi-processors is more complex. The performance parameters, such as execution efficiency and total time of completion, need to be considered [4,5,6,7]. The combination optimization algorithm, which is widely used in task scheduling problem, is genetic algorithm (GA) [11,12,13]. The parameters of genetic algorithm are complicated to configure, and the effectiveness of the crossover and mutation operations decreases when the number of tasks increases, and the “premature” phenomenon can be triggered by the population initialization of the individuals [12, 14,15,16,17,18]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
