Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments

European Journal of Operational Research

Volumn 218, Issue 1, 2012, Pages 7-20

  Du Plessis, Mathys C ^a   Engelbrecht, Andries P ^a  

^a UNIVERSITY OF PRETORIA   (South Africa)

Author keywords

Differential evolution; Dynamic environments; Evolutionary computation; Optimization

Indexed keywords

COMPETITIVE POPULATION; DIFFERENTIAL EVOLUTION; DIFFERENTIAL EVOLUTION ALGORITHMS; DYNAMIC ENVIRONMENTS; DYNAMIC OPTIMIZATION PROBLEMS; EMPIRICAL RESULTS; EVOLUTIONARY COMPUTATIONS; MULTI-POPULATION DE ALGORITHMS; REINITIALIZATION; SEARCH SPACES;

OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 83955164254     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2011.08.031     Document Type: Article

References (52)

1. R. Angira, and A. Santosh Optimization of dynamic systems: A trigonometric differential evolution approach Computers and Chemical Engineering 31 9 2007 1055 1063 (Pubitemid 46776830)
2. Angira, R.; Santosh, A.; 2008. A modified trigonometric differential evolution algorithm for optimization of dynamic systems. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1463-1468.
3. T.M. Blackwell, and P.J. Bentley Dynamic search with charged swarms W.B. Langdon, Genetic and Evolutionary Computation Conference 2002 Morgan Kaufmann 19 26
4. T. Blackwell, and J. Branke Multiswarm optimization in dynamic environments Applications of Evolutionary Computing 3005 2004 489 500
5. T. Blackwell, and J. Branke Multiswarms, exclusion, and anti-convergence in dynamic environments IEEE Transactions on Evolutionary Computation 10 4 2006 459 472 (Pubitemid 44251378)
6. S. Boettcher, and A.G. Percus Extremal optimization: Methods derived from co-evolution W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, R.E. Smith, Proceedings of the Genetic and Evolutionary Computation Conference vol. 1 1999 Morgan Kaufmann Orlando, Florida, USA 825 832
7. J. Branke Evolutionary Optimization in Dynamic Environments 2002 Kluwer Academic Publishers Norwell, MA, USA
8. Branke, J.; 2007. The Moving Peaks Benchmark. .
9. J. Branke, and H. Schmeck Designing evolutionary algorithms for dynamic optimization problems S. Tsutsui, A. Ghosh, Theory and Application of Evolutionary Computation: Recent Trends 2003 Springer 239 262
10. J. Branke, T. Kaussler, C. Schmidt, and H. Schmeck A multi-population approach to dynamic optimization problems Adaptive Computing in Design and Manufacturing 2000 Springer 299 308
11. J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems IEEE Transactions on Evolutionary Computation 10 6 2006 646 657 (Pubitemid 46445554)
12. J. Brest, A. Zamuda, B. Boškovic, M.S. Maučec, and V. Žumer Dynamic optimization using self-adaptive differential evolution CEC'09: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation 2009 IEEE Press Piscataway, NJ, USA 415 422
13. Cobb, H.G.; 1990. An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments, Navy Center for Applied Research in Artificial Intelligence, Technical Report, AIC-90-001.
14. Das, S.; Konar, A.; Chakraborty, U.; 2005. An improved differential evolution scheme for noisy optimization problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1691-1698. (Pubitemid 41496053)
15. H.-Y. Fan, and J. Lampinen A trigonometric mutation operation to differential evolution Journal of Global Optimization 27 2003 105 129
16. T.B. Gibbon, L. Wu, D.W. Waswa, A.B. Conibear, and A.W.R. Leitch Polarization mode dispersion compensation for the south african optical-fibre telecommunication network South African Journal of Science 2008 104 119
17. J.J. Grefenstette Evolvability in dynamic fitness landscapes: A genetic algorithm approach Congress on Evolutionary Computation vol. 3 1999 IEEE 2031 2038
18. Hu, X.; Eberhart, R.; 2002. Adaptive particle swarm optimisation: Detection and response to dynamic systems. In: Congress on Evolutionary Computation, pp. 1666-1670.
19. S. Janson, and M. Middendorf A hierarchical particle swarm optimizer Congress on Evolutionary Computation 2003 IEEE 770 776
20. S. Janson, and M. Middendorf A hierarchical particle swarm optimizer for dynamic optimization problems G.R. Raidl, Applications of Evolutionary Computing LNCS vol. 3005 2004 Springer 513 524
21. Y. Jin, and J. Branke Evolutionary optimization in uncertain environments - A survey IEEE Transactions on Evolutionary Computation 9 3 2005 303 317 (Pubitemid 40871134)
22. P. Kaelo, and M. Ali A numerical study of some modified differential evolution algorithms European Journal of Operational Research 169 3 2006 1176 1184 (Pubitemid 41492039)
23. J. Kennedy, and R. Eberhart Particle swarm optimization International Conference on Neural Networks 1995 IEEE 1942 1948
24. X. Li, and K.H. Dam Comparing particle swarms for tracking extrema in dynamic environments Congress on Evolutionary Computation vol. 3 2003 IEEE 1772 1779
25. C. Li, and S. Yang A generalized approach to construct benchmark problems for dynamic optimization SEAL'08: Proceedings of the 7th International Conference on Simulated Evolution and Learning 2008 Springer-Verlag Berlin, Heidelberg 391 400
26. X. Li, J. Branke, and T. Blackwell Particle swarm with speciation and adaptation in a dynamic environment GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation 2006 ACM New York, NY, USA 51 58 (Pubitemid 44611294)
27. Li, C.; Yang, S.; Nguyen, T.T.; Yu, E.L.; Yao, X.; Jin, Y.; Beyer, H.G.; Suganthan, P.N.; 2008. Benchmark Generator for CEC2009 Competition on Dynamic Optimization, Technical Report, University of Leicester, University of Birmingham, Nanyang Technological University.
28. Liu, B.; Zhang, X.; Ma, H.; 2008. Hybrid differential evolution for noisy optimization. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 587-592.
29. R.I. Lung, and D. Dumitrescu A new collaborative evolutionary-swarm optimization technique GECCO'07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation 2007 ACM New York, NY, USA 2817 2820 (Pubitemid 47291079)
30. R.I. Lung, and D. Dumitrescu Evolutionary swarm cooperative optimization in dynamic environments Natural Computing: An International Journal 9 1 2010 83 94
31. R. Mendes, and A. Mohais DynDE: A differential evolution for dynamic optimization problems Congress on Evolutionary Computation 2005 IEEE 2808 2815 (Pubitemid 41496198)
32. N. Mori, H. Kita, and Y. Nishikawa Adaptation to a changing environment by means of the thermodynamical genetic algorithm H.-M. Voigt, Parallel Problem Solving from Nature LNCS vol. 1141 1996 Springer-Verlag Berlin 513 522 (Pubitemid 126128339)
33. N. Mori, S. Imanishi, H. Kita, and Y. Nishikawa Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm T. Bäck, International Conference on Genetic Algorithms 1997 Morgan Kaufmann 299 306
34. N. Mori, H. Kita, and Y. Nishikawa Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm A.E. Eiben, T. Bäck, M. Schoenauer, H.-P. Schwefel, Parallel Problem Solving from Nature LNCS vol. 1498 1998 Springer 149 158 (Pubitemid 128145946)
35. R. Morrison Designing Evolutionary Algorithms for Dynamic Environments 2004 Springer
36. Moser, I.; 2007. Personal communication.
37. I. Moser, and T. Hendtlass A simple and efficient multi-component algorithm for solving dynamic function optimisation problems Congress on Evolutionary Computation 2007 IEEE 252 259
38. M.G. Omran, A.P. Engelbrecht, and A. Salman Bare bones differential evolution European Journal of Operational Research 196 1 2009 128 139
39. F. Oppacher, and M. Wineberg The shifting balance genetic algorithm: Improving the GA in a dynamic environment W. Banzhaf, Genetic and Evolutionary Computation Conference (GECCO) vol. 2 1999 Morgan Kaufmann San Francisco 504 510
40. D. Parrott, and X. Li A particle swarm model for tracking multiple peaks in a dynamic environment using speciation Congress on Evolutionary Computation 2004 IEEE 98 103
41. D. Parrott, and X. Li Locating and tracking multiple dynamic optima by a particle swarm model using speciation IEEE Transactions on Evolutionary Computation 2006 440 458 (Pubitemid 44251377)
42. K. Price, R.M. Storn, and J.A. Lampinen Differential Evolution - A Practical Approach to Global Optimization 2005 Springer
43. C.L. Ramsey, and J.J. Grefenstette Case-based initialization of genetic algorithms S. Forrest, International Conference on Genetic Algorithms 1993 Morgan Kaufmann 84 91
44. A. Salman, A.P. Engelbrecht, and M.G. Omran Empirical analysis of self-adaptive differential evolution European Journal of Operational Research 183 2 2007 785 804 (Pubitemid 46891141)
45. B. Sareni, and L. Krahenbuhl Fitness sharing and niching methods revisited IEEE Transactions on Evolutionary Computation 2 3 1998 97 106 (Pubitemid 128748407)
46. R. Storn On the usage of differential evolution for function optimization Biennial Conference of the North American Fuzzy Information Processing Society 1996 IEEE 519 523
47. R. Storn, and K. Price Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces Journal of Global Optimization 11 1997 341 359 (Pubitemid 127502202)
48. R. Thomsen Multimodal optimization using crowding-based differential evolution CEC'04: Proceedings of the 2004 Congress on Evolutionary Computation 2004 IEEE Press 382 1389
49. Trojanowski, K.; 2007. B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM'07, pp. 143-148.
50. R.K. Ursem Multinational GA optimization techniques in dynamic environments D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee, H.-G. Beyer, Genetic and Evolutionary Computation Conference 2000 Morgan Kaufmann 19 26
51. F. Vavak, K. Jukes, and T.C. Fogarty Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search T. Bäck, International Conference on Genetic Algorithms 1997 Morgan Kaufmann 719 726
52. S. Yang Memory-based immigrants for genetic algorithms in dynamic environments GECCO '05: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation 2005 ACM New York, NY, USA 1115 1122 (Pubitemid 43226422)