This paper presents the hybrid approach of two natures inspired metaheuristic algorithms; simulated annealing and Particle Swarm Optimization (PSO) is used for solving optimization problems. The population-based stochastic global search algorithm is known as Cuckoo Search. The job scheduling (JS) is one of the most studied operational research and computer science. Research is produced to a large number of techniques to resolve this problem, the results obtained by is when compared to other techniques. This paper propose a hybrid algorithm, namely PSO-SA, based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. The hybrid PSO algorithm is not only in the structure of the algorithm, but also the search mechanism provides a powerful way to solve JSSP. Experimental results are examined with the job scheduling problem and the results show a promising performance of this algorithm. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the JSSP.
1. Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013). 2. Tsung- Lieh Lin et al., An efficient job-shop scheduling algorithm based on particle swarm optimization, Expert Systems with Applications, Volume 37, Issue 3, 15 March 2010, Pages 2629-2636. 3. Xinyu Shao et al., Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem,The International Journal of Advanced Manufacturing Technology ,August 2013, Volume 67, Issue 9-12, pp 2885-2901. 4. Xin-She Yang, Xingshi He , Swarm Intelligence and Evolutionary Computation: Overview and Analysis, Recent Advances in Swarm Intelligence and Evolutionary Computation Studies in Computational Intelligence Volume 585, 2015, pp 1-23 Dec 2014. 5. Albodour, R, James, A & Yaacob, N 2012, ‘High level qos-driven model for grid applications in a simulated environment’, Future Generation Computer Systems vol. 28, no. 7, pp. 1133-1144. 6. D.Y. Sha, Hsing-Hung Lin ,” A Multi-Objective PSO for Job-Shop Scheduling Problems. Expert Systems With Applications”, Volume 37, Issue 2, March 2010, Pages 1065–1070. 7. Hazem Ahmed,Janice Glasgow,”Swarm Intelligence: Concepts, Models and Applications”, School of Computing,Queen’s University, Kingston, Ontario, Canada K7L3N6, February 2012. 8. P. Lim, L. C. Jain, S. Dehuri, ”Innovations in Swarm Intelligence. Studies in Computational Intelligence”, Vol. 248, Springer, 2009. 9. Xin-She Yang,Suash Deb, Cuckoo search: recent advances and applications, Neural Computing and Applications, January 2014, Volume 24, Issue 1, pp 169-174,Mar 2013. 10. Derrac, J., Garcia, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1, 3–18 (2011). 11. Xin-She Yang, Swarm intelligence based algorithms: a critical analysis, Evolutionary Intelligence April 2014, Volume 7, Issue 1, pp 17-28 Date: 17 Dec 2013.