Open Access   Article

Multiprocessor Scheduling using Krill Herd Algorithm (KHA)

S.K. Nayak1 , C.S. Panda2 , S.K. Padhy3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 7-17, Jun-2018


Online published on Jun 30, 2018

Copyright © S.K. Nayak, C.S. Panda, S.K. Padhy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S.K. Nayak, C.S. Panda, S.K. Padhy, “Multiprocessor Scheduling using Krill Herd Algorithm (KHA)”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.7-17, 2018.

MLA Style Citation: S.K. Nayak, C.S. Panda, S.K. Padhy "Multiprocessor Scheduling using Krill Herd Algorithm (KHA)." International Journal of Computer Sciences and Engineering 6.6 (2018): 7-17.

APA Style Citation: S.K. Nayak, C.S. Panda, S.K. Padhy, (2018). Multiprocessor Scheduling using Krill Herd Algorithm (KHA). International Journal of Computer Sciences and Engineering, 6(6), 7-17.

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This paper manages the issue of Multiprocessor scheduling Problem is one of the most challenging problems in distributed computing system. Many researchers solved the multiprocessor scheduling problem as static. But in this paper uses the dynamic multiprocessor scheduling problem which is an advanced area. Dynamic allocation strategies can be connected to huge arrangements of genuine applications that can be planned in a way that takes into account deterministic execution. In the first place, here defines the Dynamic Multiprocessor scheduling, which is an optimization problem, after that it optimizes the execution time of various tasks assigned to the processors with a Krill Herd Algorithm (KHA). In recent times, a robust meta-heuristic optimization algorithm, known as Krill Herd, which is used for global optimization to enhance the execution of the multiprocessor scheduling problem but other traditional algorithms stuck in local optimization. In this paper with the end goal of comparison, contemporary methodologies utilizing Genetic Algorithm (GA), Bacteria Foraging Optimization (BFO) and Genetic based Bacteria Foraging (GBF) found in the literature. Here, it demonstrates the better performance of Krill Herd Algorithm with the above mentioned methods by simulation process.

Key-Words / Index Term

Multiprocessor scheduling, Optimization problem, Krill Herd Algorithm (KHA)


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