Multidimensional Manhattan distance-based elimination fireworks algorithm for optimal multicore multitask scheduling

Xiao Gong; Liang Ding

doi:10.1117/12.2689603

10 August 2023 Multidimensional Manhattan distance-based elimination fireworks algorithm for optimal multicore multitask scheduling

Xiao Gong, Liang Ding

Author Affiliations +

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481F (2023) https://doi.org/10.1117/12.2689603
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In order to enhance the ability of the robot operating system to execute multiple tasks in parallel and fully exploit the parallel performance of multi-core processors, a method to optimize the multitask scheduling strategy on multiple cores is proposed, namely, the elimination-based fireworks algorithm based on multidimensional Manhattan distance. An edge sparkle is introduced in EFWAMMD to enable each iteration of the fireworks algorithm to select the fireworks at the most edge position in the feasible domain in order to improve the optimal value search domain range, and an adaptation value calculation method based on the multidimensional Manhattan distance is proposed as the multicore task scheduling adaptation value to narrow the search range of the fireworks algorithm and use the elimination roulette rule to achieve efficient task scheduling and optimise the problem of high complexity and long running time of traditional fireworks algorithms. Comparison experiments with GA and FWA algorithms show that EFWAMMD has a significant improvement in the efficiency of task scheduling and can improve the task processing throughput of multicore processors

Citation Download Citation

Xiao Gong and Liang Ding "Multidimensional Manhattan distance-based elimination fireworks algorithm for optimal multicore multitask scheduling", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481F (10 August 2023); https://doi.org/10.1117/12.2689603

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