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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

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.717

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.

BibTex Style Citation:
@article{Nayak_2018,
author = {S.K. Nayak, C.S. Panda, S.K. Padhy},
title = {Multiprocessor Scheduling using Krill Herd Algorithm (KHA)},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {7-17},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2135},
doi = {https://doi.org/10.26438/ijcse/v6i6.717}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.717}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2135
TI - Multiprocessor Scheduling using Krill Herd Algorithm (KHA)
T2 - International Journal of Computer Sciences and Engineering
AU - S.K. Nayak, C.S. Panda, S.K. Padhy
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 7-17
IS - 6
VL - 6
SN - 2347-2693
ER -

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Abstract

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)

References

[1] Sasmita Kumari Nayak, Sasmita Kumari Padhy, Siba Prasada Panigrahi, “A Novel algorithm for dynamic task scheduling”, Future Generation Computer Systems, 2012, Volume 28, Issue 5, Pages 709-717.
[2] Fatma A. Omara, Mona M. Arafa, Genetic algorithms for multiprocessor scheduling problem, Journal of Parallel and Distributed Computing 70 (1) (2010) 13–22.
[3] Orhan Engin, Gülşad Ceran, Mustafa K. Yilmaz, An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems, Applied Soft Computing 11 (3) (2011) 3056–3065.
[4] Savaş Balin, Non-identical parallel machine scheduling using genetic algorithm, Expert Systems with Applications 38 (6) (2011) 6814–6821.
[5] Tzu-Chiang Chiang, Po-Yin Chang, and Yueh-Min Huang, “Multi-Processor Tasks with Resource and Timing Constraints Using Particle Swarm Optimization”, IJCSNS International Journal of Computer Science and Network Security, Vol.6 No.4 (2006), pp. 71-77.
[6] Holland J. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press; 1975.
[7] Al-Tabtabai H, Alex PA. Using genetic algorithms to solve optimization problems in construction. Eng Constr Archit Manage 1999;6(2):121–32.
[8] Grierson DE, Khajehpour S. Method for conceptual design applied to office buildings. J Comput Civil Eng 2002;16(2):83–103.
[9] Joglekar A, Tungare M. Genetic algorithms and their use in the design of evolvable hardware. http://www.manastungare.com/ articles/genetic/genetic-algorithms.pdf; 2003, accessed on May 20, 2004, 15 p.
[10] Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
[11] Gandomi AH, Yang X-S, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1): 191–200. doi:10.1016/j.camwa.2011.11.010
[12] Khazraee S, Jahanmiri A, Ghorayshi S (2011) Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution. Neural Comput Appl 20(2):239–248. doi:10.1007/s00521-010- 0364-x
[13] Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948
[14] Chen D, Zhao C, Zhang H (2011) An improved cooperative particle swarm optimization and its application. Neural Comput Appl 20(2):171–182. doi:10.1007/s00521-010-0503-4
[15] Talatahari S, Kheirollahi M, Farahmandpour C, Gandomi AH (2012) A multi-stage particle swarm for optimum design of truss structures. Neural Comput Appl. doi:10.1007/s00521-012-1072-5
[16] Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: Geotechnical and Earthquake Engineering Problems. Neural Comput Appl 21 (1):189–201
[17] Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239. doi:10.1016/j.ins.2011.07.026
[18] Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713
[19] Wang G, Guo L, Duan H, Liu L, Wang H (2012) Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm. J Sens Actuat Netw 1(2):86–96. doi: 10.3390/jsan1020086
[20] Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483
[21] Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2012) Bat algorithm for constrained optimization tasks. Neural Comput Appl. doi:10.1007/s00521-012-1028-9
[22] Gandomi AH, Yang X-S, Alavi AH (2012) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput Ger. doi:10.1007/s00366-011-0241-y
[23] Gandomi AH, Talatahari S, Yang XS, Deb S (2012) Design optimization of truss structures using cuckoo search algorithm. Struct Des Tall Spec. doi:10.1002/tal.1033
[24] Wang G, Guo L, Duan H, Wang H, Liu L, ShaoM(2012) A hybrid meta-heuristic DE/CS algorithm for UCAV three-dimension path planning. Sci World J 2012:1–11. doi:10.1100/2012/583973
[25] Yang X-S, Sadat Hosseini SS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186. doi:10.1016/j.asoc.2011.09.017
[26] Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336. doi:10.1016/j.compstruc.2011.08.002
[27] Talatahari S, Gandomi AH, Yun GJ (2012) Optimum design of tower structures using Firefly Algorithm. Struct Des Tall Spec
[28] Wang G, Guo L, Duan H, Liu L, Wang H (2012) A modified firefly algorithm for UCAV path planning. Int J Hybrid Inf Technol 5(3):123–144
[29] Gandomi AH, Alavi AH (2012) Krill Herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010
[30] Wang Chen, Yan-jun Shi, Hong-fei Teng, Xiao-ping Lan, Li-chen Hu, An efficient hybrid algorithm for resource-constrained project scheduling, Information Sciences 180 (6) (2010) 1031–1039.
[31] Peng-Yeng YinT, Shiuh-Sheng Yu, Pei-Pei Wang, Yi-Te Wang, “A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems”, science direct, Computer Standards & Interfaces 28 (2006) 441–450
[32] S.N. Sivanandam. "Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation", International Journal of Bio-Inspired Computation, 2009.
[33] Hofmann EE, Haskell AGE, Klinck JM, Lascara CM. Lagrangian modelling studies of Antarctic krill (Euphasia superba) swarm formation. ICES J Mar Sci 2004;61:617–31.
[34] Mani Ashouri, Seyed Mehdi Hosseini, “Application of Krill herd and Water cycle algorithms on Dynamic Economic Load Dispatch Problem”, IJIEEB, 2014, (4), pp: 12-19.
[35] Wang, G., Guo, L., Wang, H., Duan, H., Liu, L., Li, J.: Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput & Applic 24, 853-871 (2014).