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International Conference on Application of Robotics in Industry using Advanced Mechanisms

ARIAM 2019: Applications of Robotics in Industry Using Advanced Mechanisms pp 1–13Cite as

Improved Crow Search Optimization for Multiprocessor Task Scheduling: A Novel Approach

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Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 5))

Abstract

Solving of multiprocessor tasks scheduling is a challenging problem in grid environment. Combination of set of tasks to be completed with defined number of processors is the key point of multiprocessor task scheduling. This paper focuses on dynamic task scheduling in heterogeneous multiprocessor system. Task assignment in multiprocessor system is an optimization problem. In this paper, two metaheuristic algorithms, named Crow Search Optimization (CSO) and the enhanced version of CSO, named Improved Crow Search Optimization (ICSO) were implemented to solve this problem. The experimental results evidenced that the proposed algorithms outperforms several other standard algorithms, such as Genetic Based Bacteria Foraging (GBF), Bacteria Foraging Optimization (BFO).

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References

  1. Vivekanandan K, Ramyachitra D (2011) A study on scheduling in grid environment. Int J Comput Sci Eng (IJCSE) 3(2). ISSN 0975-3397

    Google Scholar 

  2. Balin S (2011) Non-identical parallel machine scheduling using genetic algorithm. Expert Syst Appl 38(6):6814–6821

    Article  Google Scholar 

  3. Engin O, Ceran G, Yilmaz MK (2011) An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Appl Soft Comput 3:3056–3065

    Article  Google Scholar 

  4. Nayak SK, Padhy SK, Panda CS (2018) Efficient multiprocessor scheduling using water cycle algorithm. In: Pant M, Ray K, Sharma T, Rawat S, Bandyopadhyay A (eds) Soft computing: theories and applications, vol 583. Advances in intelligent systems and computing. Springer, Singapore

    Google Scholar 

  5. Nayak SK, Padhy SK, Panigrahi SP (2012) A novel algorithm for dynamic task scheduling. Future Gener Comput Syst 28:709–717

    Article  Google Scholar 

  6. Prajapati HB, Shah VA (2014) Scheduling in grid computing environment. In: IEEE fourth international conference on advanced computing & communication technologies, pp 315–324

    Google Scholar 

  7. Elsadek A, Wells BE (1999) A heuristic model for task allocation in heterogeneous distributed computing systems. Int J Comput Appl 6(1):1–36

    Google Scholar 

  8. Ahmad SG, Liew CS, Munir EU, Fonga AT, Khan SU (2016) A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J Parallel Distrib Comput 87:80–90

    Article  Google Scholar 

  9. Yang J, Xu H, Pan L, Jia P, Long L, Jie M (2011) Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments. Appl Soft Comput 11:3297–3310

    Article  Google Scholar 

  10. Josephson J, Ramesh R (2018) A novel algorithm for real time task scheduling in multiprocessor environment. Springer Science+Business Media, LLC, part of Springer Nature

    Google Scholar 

  11. Alhussian H, Abdulkadir SJ, Zakaria N, Patel A, Alzahrani A. Practical performance analysis of real-time multiprocessor scheduling algorithms. J Fund Appl Sci. ISSN 1112-9867

    Google Scholar 

  12. Tripathy B, Dash S, Padhy SK (2015) Dynamic task scheduling using a directed neural network. J Parallel Distrib Comput 75:101–106

    Article  Google Scholar 

  13. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  14. Díaz P, Cisneros MP, Cuevas E, Avalos O, Gálvez J, Hinojosa S, Zaldivar D (2018) An improved crow search algorithm applied to energy problems. Energies 11:571

    Article  Google Scholar 

  15. Asli BZ, Haddad OB, Chu X (2018) Crow search algorithm (CSA). In: Advanced optimization by nature-inspired algorithms. Studies in computational intelligence, vol 720. Springer Nature Singapore Pte Ltd. https://doi.org/10.1007/978-981-10-5221-7_14

    Google Scholar 

  16. Dos Santos Coelho L et al (2016) Modified crow search approach applied to electromagnetic optimization. In: IEEE conference on electromagnetic field computation. IEEE

    Google Scholar 

  17. Sharma A, Kaur M (2015) An efficient task scheduling of multiprocessor using genetic algorithm based on task height. J Inf Technol Softw Eng. ISSN 2165-7866

    Google Scholar 

  18. Thanushodi K, Debba K (2011) On performance analysis of hybrid algorithm (Improved PSO with simulated annealing) with GA, PSO for multiprocessor job scheduling. WSEAS Trans Comput. ISSN 1109-2750

    Google Scholar 

  19. Kahramana C et al (2010) Multiprocessor task scheduling in multistage hybrid flow-shops: a parallel greedy algorithm approach. Appl Soft Comput 10:1293–1300

    Article  Google Scholar 

  20. Omara FA et al (2010) Genetic algorithms for task scheduling problem. J Parallel Distrib Comput 70:13–22

    Article  MATH  Google Scholar 

  21. Zomaya AY et al (1999) Genetic scheduling for parallel processor systems: comparative studies and performance issues. IEEE Trans Parallel Distrib Syst 10(8):795–812

    Article  Google Scholar 

  22. Behnamiana J et al (2014) Multi-objective fuzzy multiprocessor flowshop scheduling. Appl Soft Comput 21:139–148

    Article  Google Scholar 

  23. Sarangi A et al (2013) Swarm intelligence based techniques for digital filter design. Appl Soft Comput

    Google Scholar 

  24. Kiyarazm O et al (2011) A new method for scheduling load balancing in multi-processor systems based on PSO. In: Second international conference on intelligent systems, modelling and simulation

    Google Scholar 

  25. Abdelhalim MB et al (2008) Task assignment for Heterogeneous multiprocessors using re-excited particle swarm optimization. In: International conference on computer and electrical engineering

    Google Scholar 

  26. Sivanandam SN et al (2007) Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia. Int J Comput Sci Appl © 2007 Techno Math Res Found 4(3):95–106

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Ronali Madhusmita Sahoo & Sasmita Kumari Padhy

Authors
  1. Ronali Madhusmita Sahoo
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  2. Sasmita Kumari Padhy
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Corresponding author

Correspondence to Sasmita Kumari Padhy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  2. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  4. Department of Production Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India

    Bibhuti Bhusan Choudhury

  5. Department of Computer Science and Engineering, Gandhi Institute for Technology, Hyderabad, Telangana, India

    S. Krishna Mohan Rao

  6. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Bighnaraj Naik

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Sahoo, R.M., Padhy, S.K. (2020). Improved Crow Search Optimization for Multiprocessor Task Scheduling: A Novel Approach. In: Nayak, J., Balas, V., Favorskaya, M., Choudhury, B., Rao, S., Naik, B. (eds) Applications of Robotics in Industry Using Advanced Mechanisms. ARIAM 2019. Learning and Analytics in Intelligent Systems, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-30271-9_1

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