Develop a sub-population Memetic algorithm for multi-objective scheduling problems

2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010

Volumn 5, Issue , 2010, Pages 579-583

  Wang, Yen Wen ^a   Liu, Chen Hao ^b   Fan, Chin Yuan ^c  

^a CHING YUN UNIVERSITY   (Taiwan)

^b KAINAN UNIVERSITY   (Taiwan)

^c MINGDAO UNIVERSITY   (Taiwan)

Author keywords

Flowshop scheduling problem; Memetic algorithm; Multi objective scheduling

Indexed keywords

ARTIFICIAL CHROMOSOMES; CROSSOVER OPERATOR; FAST CONVERGENCE; FLOW SHOP SCHEDULING PROBLEM; HEURISTIC SEARCH; LOCAL SEARCH HEURISTICS; MEMETIC ALGORITHMS; MULTI OBJECTIVE; MULTI-OBJECTIVE PROBLEM; MULTI-OBJECTIVE SCHEDULING; MULTI-OBJECTIVE SCHEDULING PROBLEMS; OPTIMIZATION PROBLEMS; PARETO SOLUTION; PROBABILITY MATRIXES; TEST INSTANCES;

CHROMOSOMES; HEURISTIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION;

CONVERGENCE OF NUMERICAL METHODS;

EID: 77952630319     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCAE.2010.5451292     Document Type: Conference Paper

References (26)

1. Affenzeller, M., 2002, New generic hybrids based upon genetic algorithms, Lecture Notes in Computer Science, 2527, 329-339.
2. Bagchi , T. P., Multiobjective Scheduling by Genetic Algorithms, Kluwer academic publishers, 1999.
3. Baker, K.R., Introduction to Sequencing and Scheduling, Wiley, New York, 1974.
4. Chang, P. C., Chen, S. H., and Lin, K. L., 2005, Two phase subpopulation genetic algorithm for parallel machine scheduling problem, Expert Systems with Applications, 29(3), 705-712.
5. Chang, P. C., Chen, S.H., and Liu, C.H., Sub-population genetic algorithm with mining gene structures for multi-objective Flowshop scheduling problems, Expert Systems with Applications, 33 (2007), 762-771
6. Chang, P.C., Wang, Y.W., and Liu, C.H., 2005, New Operators for Faster Convergence and Better Solution Quality in Modified Genetic Algorithm, Lecture Notes in Computer Science, 3611, 983-991.
7. Cochran, J. K., Horng, S. M., and Fowler, J. W., 2003, A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines, Computers and Operations Research, 30, 1087-1102.
8. Coello, C. A., Pulido, G. T., and Lechuga, M. S., 2004, Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256-279.
9. Deb, K., Amrit, P., Sameer, A., and Meyarivan, T., 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182-197. (Pubitemid 34555372)
10. Garey, M.R., Johnson, D.S., and Sethi, R., 1976, The complexity of Flowshop and jobshop scheduling, Math. of Operation Research. 1, 117-129.
11. Harik, G.R., Lobo, F.G., and Goldberg, D.E., 1999, The compact genetic algorithm, IEEE Transactions of Evolution Computing, 3 (4), 287-297.
12. Hu, J., Goodman, E., Seo, K., Fan, Z., and Rosenberg, R., 2005, The hierarchical fair competition framework for sustainable evolutionary algorithms, Evolutionary Computation, 13(2), 241-277. (Pubitemid 40892154)
13. Ishibuchi, H., Yoshida, T. and Murata, T., 2003, Balance between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling, IEEE Trans on Evolutionary Computation, 7(2), 204-223.
14. Knowles, J. D., and Corne, D. W., 2002, On metrics for comparing non-dominated sets, In Proceedings of the 2002 congress on evolutionary computation conference, 711-716.
15. Larranaga, P., and Lozano, J.A., 2001, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer, Norwell.
16. Lis, J., and Eiben, A. E., 1997, A multisexual genetic algorithm for multi-criteria optimization. In Proceedings of the 4th IEEE conference on evolutionary computation, 59-64.
17. Lozano, J.A., Larranaga, P., Inza, I., and Bengoetxea, E., 2006, Towards a New Evolutionary Computation, Springer.
18. Mostaghim, S., and Teich, J., 2004, Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization, Evolutionary Computation, 2, 1404-1411.
19. Pelikan, M., Goldberg, D.E., and Lobo, F.G., 2002, A survey of optimization by building and using probabilistic models, Computational Optimization and Applications, 21 (1), 5-20. (Pubitemid 34172679)
20. Rastegar, R., and Hariri, A., 2006, A step forward in studying the compact genetic algorithm, IEEE Transactions of Evolution Computing, 14 (3), 277-289.
21. Schaffer, J. D., 1985, Multiple objective optimizations with vector evaluated genetic algorithms. In Proceedings of 1st international conference on genetic algorithms, 93-100.
22. Taillard, E., "Benchmarks for basic scheduling problems," European Journal of Operational Research, 64(2), pp.278-285, 1993.
23. Zhang, Q., Sun, J., and Tsang, E., 2005, An evolutionary algorithm with guided mutation for the maximum clique problem, Evolutionary of Computing, 9 (2), 192-200.
24. Zitzler, E., and Thiele, L., 1999, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3(4), 257-271. (Pubitemid 30544879)
25. Zitzler, E., Laumanns, M., & Bleuler, S., 2004, A tutorial on evolutionary multi-objective optimization, In Proceedings of the workshop on multiple objective metaheuristics.
26. Zitzler, E., Laumanns, M., and Thiele, L., 2001, SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland.