Hybrid Ant Colony Optimization and Cuckoo Search Algorithm for Job Scheduling

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

Job scheduling is a type of combinatorial optimization problem. In this paper, we propose a Hybrid algorithm which combines the merits of ACO and Cuckoo Search. The major problem in the ACO is that, the ant will walk through the path where the chemical substances called pheromone is deposited. This acts as if it lures the artificial ants. Cuckoo search can perform the local search more efficiently and there is only a single parameter apart from the population size. It minimizes the makespan and the scheduling can be used in scientific computing and high power computing.

Keywords

Job Scheduling Ant Colony Optimization Cuckoo Search 

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References

  1. 1.
    Surekha, S.: PSO and ACO based approach for solving combinatorial Fuzzy Job Shop Scheduling. Int. J. Comp. Tech. Appl. 2(1), 112–120 (2010)MathSciNetGoogle Scholar
  2. 2.
    Ferrandi, F.: Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems. IEEE Transactions on Computer-Aided Design of Intergrated Circuits and Systems 29(6) (2010)Google Scholar
  3. 3.
    Guo, S., Huang, H.-Z.: Grid Service Reliability Modeling and Optimal Task Scheduling Considering Fault Recovery. IEEE Transactions on Realiability 60(1) (2011)Google Scholar
  4. 4.
    Venkatesan, S., Dhavachelvan, P., Chellapan, C.: Performance analysis of mobile agent failure recovery in e-service applications. International Journal of Computer Standards and Interfaces 32(1-2), 38–43 (2005) ISSN:0920-5489CrossRefGoogle Scholar
  5. 5.
    Venkatesan, S., Chellapan, C., Vengattaraman, T., Dhavachelvan, P., Vaish, A.: Advanced Mobile Agent Security Models for Code Integrity and Malicious Availability Check. International Journal of Network and Computer Applications 33(6), 661–671 (2010)CrossRefGoogle Scholar
  6. 6.
    Tan, Q., Chen, H.-P.: Two-agent scheduling on a single batch processing machine with non-identical job sizes. In: Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC (2011)Google Scholar
  7. 7.
    Tavakkoli-Moghaddam, Azarkish: A new hybrid mutli-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Elsevier Expert Systems with Applications (2011)Google Scholar
  8. 8.
    Ahn, C.W., An, J.: Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs. Elsevier Information Sciences (2010)Google Scholar
  9. 9.
    Yang, X., Yuan, J.: An improved WM method based on PSO for electric load forecasting. Elsevier Expert Systems with Applications (2010)Google Scholar
  10. 10.
    Victer Paul, P., Saravanan, N., Jayakumar, S.K.V., Dhavachelvan, P., Baskaran, R.: QoS enhancements for global replication management in peer to peer networks. Future Generation Computer Systems 28(3), 573–582 (2012)CrossRefGoogle Scholar
  11. 11.
    Abirami, S., Baskaran, R., Dhavachelvan, P.: A survey of Keyword spotting techniques for Printed Document Images. Artificial Intelligence Review 35(2), 119–136 (2010)Google Scholar
  12. 12.
    Bae, C., Yeh, W.-C.: Elsevier Expert Expert Systems with Applications. Feature selection with Intelligent Dynamic Swarm and Rough Set (2010)Google Scholar
  13. 13.
    Sha, Lin, H.-H.: A Multi-objective PSO for job-shop scheduling problems. Elsevier Expert Systems with Applications (2010)Google Scholar
  14. 14.
    Tao, Q., Chang, H.-Y.: A rotary Chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Elsevier Computers & Operations Research (2011)Google Scholar
  15. 15.
    Dhavachelvan, P., Uma, G.V.: Complexity Measures For Software Systems: Towards Multi-Agent Based Software Testing. In: Proceedings-2005 International Conference on Intelligent Sensing and Information Processing, ICISIP 2005, Art. no. 1529476, pp. 359–364 (2005)Google Scholar
  16. 16.
    Vengattaraman, T., Abiramy, S., Dhavachelvan, P., Baskaran, R.: An Application Perspective Evaluation of Multi-Agent System in Versatile Environments. International Journal on Expert Systems with Applications 38(3), 1405–1416 (2011)CrossRefGoogle Scholar
  17. 17.
    Vengattaraman, T., Dhavachelvan, P.: An Agent-Based Personalized E-Learning Environment: Effort Prediction Perspective. In: IEEE International Conference on Intelligent Agent & Multi-Agent Systems, IAMA 2009 (2009) ISBN: 978 1-4 244-4710-7Google Scholar
  18. 18.
    Hu, X.-M., Zhang, J.: SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization. IEEE Transactions on Systems, Man, and Cybernetics 40(6) (2010)Google Scholar
  19. 19.
    Zhang, Z., Zhang, J., Li, S.: A Modified Ant Colony Algorithm for the Job Shop Scheduling Problem to Minimize Makespan. IEEE Explore (2010)Google Scholar
  20. 20.
    Zhan, Z.-H., Zhang, J.: An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem. IEEE Transactions on Intelligent Transaction Systems 11(2) (2010)Google Scholar
  21. 21.
    Victer Paul, P., Vengattaraman, T., Dhavachelvan, P.: Improving efficiency of Peer Network Applications by formulating Distributed Spanning Tree. In: Proceedings - 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010, Art. no. 5698439, pp. 813–818 (2010)Google Scholar
  22. 22.
    Saleem Basha, M.S., Dhavachelvan, P.: Web Service Based Secure E-Learning Management System - EWeMS. International Journal of Convergence Information Technology 5(7), 57–69 (2010) ISSN: 1975 9320CrossRefGoogle Scholar
  23. 23.
    Dhavachelvan, P., Uma, G.V., Venkatachalapathy, V.S.K.: A New Approach in Development of Distributed Framework for Automated Software Testing Using Agents. International Journal on Knowledge–Based Systems 19(4), 235–247 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Bharatiyar UniversityCoimbatoreIndia
  2. 2.Department of Computer SciencePondicherry UniversityPondicherryIndia

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