Assignments acceptance strategy in a Modified PSO Algorithm to elevate local optima in solving class scheduling problems

Proceedings - CSPA 2010: 2010 6th International Colloquium on Signal Processing and Its Applications

Volumn , Issue , 2010, Pages

  Aziz, Mohd Azri Abdul ^a   Taib, Mohd Nasir ^a   Hussin, Naimah Mohd ^a  

^a UNIVERSITI TEKNOLOGI MARA   (Malaysia)

Author keywords

Particle swarm optimization; Scheduling; Timetabling

Indexed keywords

CLASS SCHEDULING; GLOBAL OPTIMUM; LOCAL OPTIMA; OBJECTIVE FUNCTIONS; OPTIMIZATION ALGORITHMS; OPTIMIZATION PROBLEMS; PENALTY REDUCTION; PSO ALGORITHMS; TIMETABLING;

ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); PROBABILITY; SIGNAL PROCESSING;

PROBLEM SOLVING;

EID: 77956500623     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSPA.2010.5545252     Document Type: Conference Paper

References (26)

1. Guo, Y.W., et al., Applications of particle swarm optimisation in integrated process planning and scheduling. Robotics and Computer- Integrated Manufacturing, 2007. In Press, Corrected Proof.
2. Sha, D.Y. and C.Y. Hsu, A new particle swarm optimization for the open shop scheduling problem. Computers & Operations Research, 2008. 35(10): p. 3243-3261.
3. Shi, X.H., et al., Particle swarm optimization-based algorithms for TSP and generalized TSP. Information Processing Letters, 2007. 103(5): p. 169-176.
4. Akjiratikarl, C., P. Yenradee, and P.R. Drake, PSO-based algorithm for home care worker scheduling in the UK. Computers & Industrial Engineering, 2007. 53(4): p. 559-583.
5. Liu, B., L. Wang, and Y.H. Jin, An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Computers & Operations Research, 2008. 35(9): p. 2791-2806.
6. Salman, A., I. Ahmad, and S. Al-Madani, Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, 2002. 26(8): p. 363-371.
7. Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proc. IEEE International Conference on Neural Networks. 1995.
8. Kang-Ping, W., et al. Particle swarm optimization for traveling salesman problem. in Proc. 2003 International Conference on Machine Learning and Cybernetics. 2003.
9. Kuo, I.H., et al., An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model. Expert Systems with Applications. In Press, Corrected Proof.
10. Yin, P.Y., et al., Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization. Applied Mathematics and Computation, 2007. 184(2): p. 407-420.
11. Lian, Z., X. Gu, and B. Jiao, A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan. Applied Mathematics and Computation, 2006. 175(1): p. 773-785.
12. Janson, S., D. Merkle, and M. Middendorf, Molecular docking with multi-objective Particle Swarm Optimization. Applied Soft Computing, 2008. 8(1): p. 666-675.
13. Liu, L., W. Liu, and D.A. Cartes, Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors. Engineering Applications of Artificial Intelligence. In Press, Corrected Proof.
14. Joshua, D.K., A.W. Richard, and C. David. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. in Proc. First International Conference on Evolutionary Multi-Criterion Optimization. 2001: Springer-Verlag.
15. Dong, C., et al. A Method of Self-Adaptive Inertia Weight for PSO. In Proc. 2008 International Conference on Computer Science and Software Engineering. 2008.
16. Shi, X.H., et al. Hybrid evolutionary algorithms based on PSO and GA. in Proc. The 2003 Congress on Evolutionary Computation (CEC '03). 2003.
17. Smith, K.A., D. Abramson, and D. Duke, Hopfield neural networks for timetabling: formulations, methods, and comparative results. Computers & Industrial Engineering, 2003. 44(2): p. 283-305.
18. Kao, Y.T. and E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 2008. 8(2): p. 849-857.
19. Xing, L.N., et al., A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem. Engineering Applications of Artificial Intelligence, 2008. 21(8): p. 1370-1380.
20. Huang, K.L. and C.J. Liao, Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput. Oper. Res., 2008. 35(4): p. 1030-1046.
21. Yagmahan, B. and M. M.Yenisey, Ant colony optimization for multiobjective flow shop scheduling problem. Comput. Ind. Eng., 2008. 54(3): p. 411-420.
22. Ping, R., et al. Application of particle swarm optimization to the train scheduling for high-speed passenger railroad planning. in Proc. IEEE International Symposium on Communications and Information Technology (ISCIT 2005). 2005.
23. Ge, H., W. Du, and F. Qian. A Hybrid Algorithm Based on Particle Swarm Optimization and Simulated Annealing for Job Shop Scheduling. in Proc. Third International Conference onNatural Computation (ICNC 2007). 2007.
24. Parsopoulos, K.E., et al. Stretching technique for obtaining global minimizers through particle swarm optimization. in Proc. Particle Swarm Optimization Workshop. 2001. Indianapolis, USA.
25. Laura, B., W. Jean-Paul, and L.D. Whitley. Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search. in Proc. Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence. 2000.
26. Aziz, M.A.A., M.N. Taib, and N. Mohd Hussin. An improved event selection technique in a modified PSO algorithm to solve class scheduling problems. in Industrial Electronics & Applications, 2009. ISIEA 2009. IEEE Symposium on. 2009.