A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

Neurocomputing

Volumn 72, Issue 16-18, 2009, Pages 3570-3579

  Cámara, Mario ^a   Ortega, Julio ^a   de Toro, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Dynamic optimization problems; Neuro dynamic programming; Parallel evolutionary algorithms; Parallel multi objective optimization

Indexed keywords

CROWDING MECHANISM; DYNAMIC ENVIRONMENTS; DYNAMIC OPTIMIZATION PROBLEMS; DYNAMIC PROBLEM; ISLAND MODEL; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATIONS; MULTI-OBJECTIVE PROBLEM; NEURO-DYNAMIC PROGRAMMING; OBJECTIVE FUNCTIONS; PARALLEL EVOLUTIONARY ALGORITHMS; PARALLEL MULTI-OBJECTIVE OPTIMIZATION; PARALLEL PROCESSINGS; PARETO FRONT; REAL-WORLD APPLICATION; SOCIO-ECONOMICS; SOLUTION QUALITY; VARIATION OPERATOR;

DYNAMIC PROGRAMMING; MATHEMATICAL OPERATORS; MULTIOBJECTIVE OPTIMIZATION; PARALLEL ALGORITHMS; SYSTEMS ENGINEERING;

EVOLUTIONARY ALGORITHMS;

ARTICLE; EVOLUTIONARY ALGORITHM; GENETIC ALGORITHM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 69249232121     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.12.041     Document Type: Article

References (39)

1. Alba E. Parallel evolutionary algorithms can achieve super-linear performance. Information Processing Letters 82 (2002) 7-13
2. Bertsekas D.P. Dynamic Programming and Optimal Control (1995), Athena Scientific, Belmont, MA
3. Bibliography on Evolutive Algorithms for Multiobjective Optimization: 〈http://www.lania.mx/~ccoello/EMOO/〉.
4. P.A.N. Bosman, Learning, anticipation and time-deception in evolutionary online dynamic optimization, in: Proceedings of the GECCO'05, 2005, pp. 39-47.
5. P.A.N. Bosman, H. La Poutré, Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case, in: Proceedings of the GECCO'07, 2007, pp. 1165-1172.
6. J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, in: Proceedings of the Congress on Evolutionary Computation, vol. 3, 1999, pp. 1875-1882.
7. Branke J., Kaussler T., Schmidt C., and Schmeck H. A multi-population approach to dynamic optimization problems. Adaptive Computing in Design and Manufacturing (2000) 299-308
8. Branke J., and Mattfeld D.C. Anticipation and flexibility in dynamic scheduling. International Journal of Production Research 43 (2005) 3103-3129
9. M. Cámara, J. Ortega, F. J. de Toro, Parallel processing for multiobjective optimization in dynamic environments, in: Proceedings of the 21st International Parallel and Distributed Processing Symposium (IPDPS '07), 2007.
10. E. Cantú Paz, Designing efficient master-slave parallel genetic algorithms, IlliGAL Report No. 97004, May 1997.
11. Cedeño W., and Vemuri V.R. On the use of niching for dynamic landscapes. IEEE International Conference on Evolutionary Computation (1997) 361-366
12. Cheng L., Subrahmanian E., and Westerberg A.W. Muli-objective decisions on capacity planning and production-inventory control under uncertainty. Industrial and Engineering Chemistry Research 43 (2004) 2192-2208
13. Cobb H.J., and Grefenstette J.J. Genetic algorithms for tracking changing environments. Proceedings of the Fifth International Conference on Genetic Algorithms (1993), Morgan Kaufmann Pub., Los Altos, CA 523-530
14. C.A. Coello, An updated survey of GA-based multi-objective optimization techniques, Technical Report Lania-RD-98-08, Laboratorio Nacional de Informática Avanzada, LANIA, Mexico, 1998.
15. Coello C.A., Van Veldhuizen D.A., and Lamont G.B. Evolutionary Algorithms for Solving Multi-Objective Problems (2002), Kluwer Academic Publishers, Dordrecht
16. Damas M., Salmerón M., Ortega J., Olivares G., and Pomares H. Parallel dynamic water supply scheduling in a cluster of computers. Concurrency and Computation: Practice and Experience 13 (2001) 1281-1302
17. Deb K., Agrawal S., Pratap A., and Meyarivan T. A fast elitist nondominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. PPSN VI: Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science Series, vol. 1917 (2000), Springer, Berlin 849-858
18. Deb K., Bhaskara Rao N., and Karthik S. Dynamic multiobjective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. Proceedings of the Fourth International Conference, EMO 2007, Lecture Notes in Computer Science, vol. 4403 (2007), Springer, Berlin 803-817
19. EvoDOP (Evolutionary algorithms for dynamic optimization problems) 〈http://www.aifb.uni-karlsruhe.de/~jbr/EvoDOP/〉.
20. Farina M., Deb K., and Amato P. Dynamic multi-objective optimization problems: test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8 (2004) 342-425
21. A. Gupta and A. Sivakumar, Pareto control in multi-objective dynamic scheduling of a stepper machine in semiconductor wafer fabrication, in: Proceedings of the 2006 Winter Simulation Conference, IEEE Computer Society, Los Alamitos, CA, USA, 2006, pp. 1749-1756.
22. J. Horn, N. Nafpliotis, Multiobjective optimization using the niched Pareto genetic algorithm, IlliGAL Report no. 93005, Illinois Genetic Algorithm Laboratory, University of Illinois at Urbana-Champaign, 1993.
23. Jin Y., and Branke J. Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9 (2005) 303-317
24. Knowles J., and Corne D. The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. In: Angeline P.J., Michalewicz Z., Schoenauer M., Yao X., and Zalzala A. (Eds). Proceedings of the Congress on Evolutionary Computation vol. 1 (1999), IEEE Press, Mayflower Hotel, Washington, DC, USA 98-105
25. L.H. Lee, S. Teng, E.P. Chew, K.W. Lye, I.A. Karimi, P. Lendermann, Y. Chen, C.H. Koh, Application of multi-objective simulation-optimization techniques to inventory management problems, in: Proceedings of the 2005 Winter Simulation Conference, 2005, pp. 1684-1691.
26. J. Mehnen, T. Wagner, G. Rudolph, Evolutionary optimization of dynamic multiobjective functions, Interner Bericht des Sonderforschungsbereichs 531 Computational Intelligence CI-204/06, Universität Dortmund, April 2006.
27. Mori N., Kita H., and Nishikawa Y. Adaptation to changing environment by means of the feedback thermodynamical genetic algorithm. Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 1498 (1998), Springer, Berlin 149-158
28. Morrison R. Performance measurement in dynamic environments. GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems (2003) 5-8
29. Shnits B., Rubinovitz J., and Sinreich D. Multicriteria dynamic scheduling for controlling a flexible manufacturing system. International Journal of Production Research 42 (2004) 3457-3472
30. Sivakumar A.I., and Gupta A.K. Online multiobjective pareto optimal dynamic scheduling of semiconductor back-end using conjunctive simulated scheduling. IEEE Transactions on Electronics Packaging Manufacturing 29 (2006) 99-109
31. Toro F., Ortega J., Ros E., Mota B., Paechter B., and Martín J.M. PSFGA: parallel processing and evolutionary computation for multi-objective optimization. Parallel Computing 30 (2004) 721-739
32. Toro F., Ros E., Mota S., and Ortega J. Evolutionary algorithms for multi-objective and multimodal optimization of diagnostic schemes. IEEE Transactions on Biomedical Engineering 53 (2006) 178-189
33. Ursem R.K. Multinational GAs Optimization techniques in dynamic environments. Genetic and Evolutionary Conference (2000), Morgan Kaufmann Pub., Los Altos, CA 19-26
34. Van Velhuizen D.A., Zydallis J.B., and Lamont G.B. Considerations in engineering parallel multi-objective evolutionary algorithms. IEEE Transactions Evolutionary Computation 7 (2003) 144-173
35. Vavak F., Jukes K., and Fogarty T.C. Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. Proceedings of the Seventh International Conference on Genetic Algorithms (1997), Morgan Kaufmann Pub., Los Altos, CA 719-726
36. Waiker K. Performance Measures for Dynamic Environments. Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 2439 (2002), Springer, Berlin 64-73
37. S. Yang, Population-based incremental learning with memory scheme for changing environments, in: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, 2005, pp. 711-718.
38. E. Zitzler, K. Deb, L. Thiele, Comparison of multi-objective evolutionary algorithms: empirical results, Technical Report 70, ETH Zurich, December 1999.
39. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization, in: K. Giannakoglou, et al. (Eds.), Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), 2002, pp. 95-100.