Abstract
Job scheduling problem is a combinatorial optimization problem in computer science in which ideal jobs are assigned to resources at particular times. Our approach is based on heuristic principles and has the advantage of both ACO and Cuckoo search. In this paper, we present a Hybrid algorithm, based on ant colony optimization (ACO) and Cuckoo Search which efficiently solves the Job scheduling problem, which reduces the total execution time. In ACO, pheromone is chemical substances that are deposited by the real ants while they walk. When it comes to solving optimization problems it acts as if it lures the artificial ants. To perform a local search, we use Cuckoo Search where there is essentially only a single parameter apart from the population size and it is also very easy to implement.
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References
Surekha, S.: PSO and ACO based approach for solving combinatorial Fuzzy Job Shop Scheduling. Int. J. Comp. Tech. Appl. 2(1), 112–120 (2010)
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)
Guo, S., Huang, H.-Z.: Grid Service Reliability Modeling and Optimal Task Scheduling Considering Fault Recovery. IEEE Transactions on Realiability 60(1) (2011)
Tan, Q., Chen, H.-P.: Two-agent scheduling on a single batch processing machine with non-identical job sizes. Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC (2011)
Azarkish, T.-M.: A new hybrid mutli-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Elsevier Expert Systems with Applications (2011)
Dhavachelvan, P., Uma, G.V.: Multi-agent based Framework for Intra-Class Testing of Object-Oriented Software. International Journal on Applied Soft Computing 5(2), 205–222 (2005)
Dhavachelvan, P., Uma, G.V.: Multi-agent Based Integrated Framework for Intra-class Testing of Object-Oriented Software. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 992–999. Springer, Heidelberg (2003)
Dhavachelvan, P., Uma, G.V.: Reliability Enhancement in Software Testing – An Agent-Based Approach for Complex Systems. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 282–291. Springer, Heidelberg (2004)
Ahn, C.W., An, J.: Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs. Elsevier Information Sciences (2010)
Yang, X., Yuan, J.: An improved WM method based on PSO for electric load forecasting. Elsevier Expert Systems with Applications (2010)
Bae, C., Yeh, W.-C.: Elsevier Expert Expert Systems with Applications. Feature Selection with Intelligent Dynamic Swarm and Rough Set (2010)
Sha, Lin, H.-H.: A Multi-objective PSO for job-shop scheduling problems. Elsevier Expert Systems with Applications (2010)
Tao, Q., Chang, H.-Y.: A rotary Chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Elsevier Computers & Operations Research (2011)
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)
Zhang, Z., Zhang, J., Li, S.: A Modified Ant Colony Algorithm for the Job Shop Scheduling Problem to Minimize Makespan. IEEE Explore (2010)
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)
Manicassamy, J., Dhavachelvan, P.: Metrics Based Performance control Over Text Mining Tools in Bio-Informatics. In: ACM International Conference on Advances in Computing, Communication and Control, ICAC3 2009, India, pp. 171–176 (2009) ISSN: 978-1-60558-351-8
Manicassamy, J., Dhavachelvan, P.: Automating diseases diagnosis in human: A Time Series Analysis. In: Proceedings of International Conference and Workshop on Emerging Trends in Technology, ICWET 2010, India, pp. 798–800 (2010) ISSN: 978-1-60558-351-8
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)
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)
Abirami, S., Baskaran, R., Dhavachelvan, P.: A survey of Keyword spotting techniques for Printed Document Images. Artificial Intelligence Review 35(2), 119–136 (2010)
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Babukarthik, R.G., Raju, R., Dhavachelvan, P. (2013). Hybrid Algorithm for Job Scheduling: Combining the Benefits of ACO and Cuckoo Search. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_49
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DOI: https://doi.org/10.1007/978-3-642-31552-7_49
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