Tabu search with multi-level neighborhood structures for high dimensional problems

Applied Intelligence

Volumn 37, Issue 2, 2012, Pages 189-206

  Hedar, Abdel Rahman ^a   Ali, Ahmed Fouad ^b  

^a Assiut University   (Egypt)

^b New Campus   (Egypt)

Author keywords

Global optimization; High dimensional problems; Metaheuristics; Tabu search

Indexed keywords

BENCHMARK FUNCTIONS; COMBINATORIAL OPTIMIZATION PROBLEMS; COMPLEX PROBLEMS; COMPUTATIONAL COSTS; COMPUTATIONAL RESULTS; COMPUTATIONAL STUDIES; DIMENSIONAL PROBLEMS; DIMENSIONAL REDUCTION; HIGH-DIMENSIONAL PROBLEMS; HIGH-QUALITY SOLUTIONS; META HEURISTICS; NEIGHBORHOOD STRUCTURE; PARTITIONING MECHANISM; SEARCH PROCESS; TABU SEARCH ALGORITHMS;

BENCHMARKING; COMBINATORIAL OPTIMIZATION; GLOBAL OPTIMIZATION; HEURISTIC ALGORITHMS; TABU SEARCH;

PROBLEM SOLVING;

EID: 84865114040     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0321-0     Document Type: Article

References (73)

1. RK Ahujaa JB Orlinb D Sharmac 2000 Very large-scale neighborhood search Int Trans Oper Res 7 301 317 1775479 10.1111/j.1475-3995.2000.tb00201.x
2. RK Ahujaa O Ergunb JB Orlinc A Punnend 2002 A survey of very large-scale neighborhood search techniques Discrete Appl Math 123 75 102 1922331 10.1016/S0166-218X(01)00338-9 (Pubitemid 34775570)
3. Ali YMB Psychological model of particle swarm optimization based multiple emotions. Appl Intell (to appear)
4. J Brest MS Maucec 2008 Population size reduction for the differential evolution algorithm Appl Intell 29 228 247 10.1007/s10489-007-0091-x
5. Z Cai W Gonga CX Lingb H Zhangc 2011 A clustering-based differential evolution for global optimization Appl Soft Comput 11 1363 1379 10.1016/j.asoc.2010.04.008
6. M Chiarandini 2008 Very large-scale neighborhood search: overview and case studies on coloring problems Stud Comput Intell 114 117 150 10.1007/978-3-540-78295-7-5
7. Conn AR, Gould NIM, Toint PL (1987) Trust-region methods. MPS-SIAM series on optimization. SIAM, Philadelphia
8. S Das A Abraham UK Chakraborty A Konar 2009 Differential evolution using a neighborhood-based mutation operator IEEE Trans Evol Comput 13 3 526 553 10.1109/TEVC.2008.2009457
9. Dolan ED Pattern search behaviour in nonlinear optimization. Thesis (1999)
10. Dreo J, Pétrowski A, Siarry P, Taillard E (2007) Metaheuristics for hard optimization. Springer, Berlin
11. A Duarte R Marti F Glover F Gortazar 2011 Hybrid scatter tabu search for unconstrained global optimization Ann Oper Res 183 95 123 2771407 1213.90200 10.1007/s10479-009-0596-2
12. O Erguna JB Orlin 2006 A dynamic programming methodology in very large scale neighborhood search applied to the traveling salesman problem Discrete Optim 3 78 85 2208961 10.1016/j.disopt.2005.10.002 (Pubitemid 43276319)
13. RA Gallego R Romero AJ Monticelli 2000 Tabu search algorithm for network synthesis IEEE Trans Power Syst 15 2 490 495 10.1109/59.867130
14. S García M Lozano F Herrera D Molina AM Sánchez 2008 Global and local real-coded genetic algorithms based on parent-centric crossover operators Eur J Oper Res 185 1088 1113 1146.90532 10.1016/j.ejor.2006.06.043 (Pubitemid 47498808)
15. R Ge 1990 A filled function method for finding a global minimizer of a function of several variables Math Program 146 191 204 (Pubitemid 20683949)
16. Glover F, Laguna M (1997) Tabu search. Kluwer, Boston
17. F Glover E Taillard D Werra 1993 A user's guide to tabu search Ann Oper Res 41 3 28 0772.90063 10.1007/BF02078647
18. M Hadi Mashinchia MA Orguna W Pedryczb 2011 Hybrid optimization with improved tabu search Appl Soft Comput 11 2 1993 2006 10.1016/j.asoc.2010.06.015
19. Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano JA, Larrañaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation. Springer, Berlin
20. P Hansen N Mladenovic JA Moreno Pérez 2010 Variable neighbourhood search: methods and applications Ann Oper Res 175 1 367 407 2593163 1185.90211 10.1007/s10479-009-0657-6
21. Hedar A, Fouad A (2009) Genetic algorithm with population partitioning and space reduction for high dimensional problems. In: Proceeding of the 2009 international conference on computer engineering and systems (ICCES09), Cairo, Egypt, pp 151-156
22. A Hedar M Fukushima 2002 Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization Optim Methods Softw 17 891 912 1953824 1065.90081 10.1080/1055678021000030084 (Pubitemid 35476479)
23. A Hedar M Fukushima 2003 Minimizing multimodal functions by simplex coding genetic algorithm Optim Methods Softw 18 265 282 1989704 1048.90168
24. A Hedar M Fukushima 2004 Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization Optim Methods Softw 19 291 308 2064248 1059.90149 10.1080/10556780310001645189
25. A Hedar M Fukushima 2006 Tabu search directed by direct search methods for nonlinear global optimization Eur J Oper Res 170 329 349 2175968 1093.90091 10.1016/j.ejor.2004.05.033 (Pubitemid 41455521)
26. Hedar A, Fukushima M (2006) Evolution strategies learned with automatic termination criteria. In: Proceedings of SCIS&ISIS 2006, Tokyo, Japan, September 20-24, 2006. Japan Society for Fuzzy Theory and Intelligent Informatics, Tokyo, pp 1126-1134
27. Hedar A, Fukushima M (2006) Directed evolutionary programming: towards an improved performance of evolutionary programming. In: Proceedings of congress on evolutionary computation, CEC 2006, IEEE world congress on computational intelligence, Vancouver, Canada, July 16-21, pp 1521-1528 (Pubitemid 47130678)
28. Hedar A, Ong BT, Fukushima M (January 2007) Genetic algorithms with automatic accelerated termination. Technical Report 2007-002, Department of Applied Mathematics and Physics, Kyoto University
29. A Hedar W Jue M Fukushima 2008 Tabu search for attribute reduction in rough set theory Soft Comput 12 909 918 1151.68643 10.1007/s00500-007-0260-1 (Pubitemid 351577724)
30. F Herrera M Lozano 2000 Two-loop real-coded genetic algorithms with adaptive control of mutation step sizes Appl Intell 13 3 187 204 10.1023/A:1026531008287
31. F Herrera M Lozano JL Verdegay 1998 Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis Artif Intell Rev 12 265 319 0905.68116 10.1023/A:1006504901164 (Pubitemid 128514089)
32. F Herrera M Lozano D Molina 2006 Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies Eur J Oper Res 169 2 450 476 2172353 1079.90176 10.1016/j.ejor.2004.08.009 (Pubitemid 41375701)
33. LM Hvattum F Glover 2009 Finding local optima of high-dimensional functions using direct search methods Eur J Oper Res 195 31 45 2474139 1156.90440 10.1016/j.ejor.2008.01.039
34. Jones DR (2001) The DIRECT global optimization algorithm. In: Floudas C, Pardalos P (eds) Encyclopedia of optimization. Kluwer Academic, Dordrecht, pp 431-440
35. Keane AJ http://www.soton.ac.uk/~ajk/bump.html. Visited on 30 March 2011
36. TG Kolda RM Lewies VJ Torczon 2003 Optimization by direct search: new perspectives on some classical and modern methods SIAM Rev 45 385 482 2046504 1059.90146 10.1137/S003614450242889
37. M Laguna R Martí 2005 Experimental testing of advanced scatter search designs for global optimization of multimodal functions J Glob Optim 33 2 235 255 1093.90092 10.1007/s10898-004-1936-z (Pubitemid 41618774)
38. CY Lee X Yao 2004 Evolutionary programming using the mutations based on the Lévy probability distribution IEEE Trans Evol Comput 8 1 13 10.1109/TEVC.2003.816583
39. YW Leung Y Wang 2001 An orthogonal genetic algorithm with quantization for global numerical optimization IEEE Trans Evol Comput 5 1 41 53 10.1109/4235.910464 (Pubitemid 32330496)
40. A Levy A Montalvo 1985 The tunneling algorithm for the global minimization of functions SIAM J Sci Stat Comput 6 15 29 773277 0601.65050 10.1137/0906002
41. Y Li X Zeng 2010 Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization Appl Intell 32 292 310 2731337 10.1007/s10489-008-0146-7
42. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings of 2005 IEEE swarm intelligence symposium, Pahes, pp 68-75 (Pubitemid 44017885)
43. JJ Liang AK Qin PN Suganthan S Baskar 2006 Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Trans Evol Comput 10 3 281 295 10.1109/TEVC.2005.857610 (Pubitemid 43821759)
44. A Linhares HH Yanasse 2010 Search intensity versus search diversity: a false trade off? Appl Intell 32 279 291 10.1007/s10489-008-0145-8
45. H Liu Z Cai Y Wang 2010 Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization Appl Soft Comput 10 2 629 640 10.1016/j.asoc.2009.08.031
46. G Liuzzi S Lucidi V Piccialli 2010 A DIRECT-based approach exploiting local minimizations for the solution of large-scale global optimization problems Comput Optim Appl 45 353 375 2594608 1187.90275 10.1007/s10589-008-9217-2
47. M Lozano F Herrera N Krasnogor D Molina 2004 Real-coded memetic algorithms with crossover hill-climbing Evol Comput 12 3 273 302 10.1162/1063656041774983 (Pubitemid 39212980)
48. MacQueen LB (1967) Some methods for classification and analysis of multivariate observations. In: LeCam LM, Neyman N (eds) Proceedings of 5th Berkeley symposium om mathematical statistics and probability. University of California Press, Berkeley, pp 281-297
49. Maniezzo V, Stutzle T, Vob S (2009) Matheuristics: hybridizing metaheuristics and mathematical programming. Annals of information systems. Springer, Berlin
50. Meyers C, Orlin J (2007) Very large-scale neighborhood search techniques in timetabling problems. In: PATAT'06 proceedings of the 6th international conference on practice and theory of automated timetabling VI. Springer, Berlin, pp 24-39
51. N Mladenovic M Drazic V Kovac M Angalovic 2008 General variable neighborhood search for the continuous optimization Eur J Oper Res 191 753 770 1156.90005 10.1016/j.ejor.2006.12.064
52. MA Montes de Oca T Stutzle M Birattari M Dorigo 2009 Frankenstein's PSO: a composite particle swarm optimization algorithm IEEE Trans Evol Comput 13 5 1120 1132 10.1109/TEVC.2009.2021465
53. H Mühlenbein D Schlierkamp-Vose 1993 Predictive models for the breeder genetic algorithm IEEE Trans Evol Comput 1 1 25 49
54. QH Nguyen Y-S Ong MH Lim 2009 A probabilistic memetic framework IEEE Trans Evol Comput 13 3 604 623 10.1109/TEVC.2008.2009460
55. N Noman H Iba 2008 Accelerating differential evolution using an adaptive local search IEEE Trans Evol Comput 12 1 107 125 10.1109/TEVC.2007.895272 (Pubitemid 351330335)
56. Pan ZJ, Kang LS (1997) An adaptive evolutionary algorithms for numerical optimization. In: Yao X, Kim JH, Furuhashi T (eds) Simulated evolutionary and learning. Lecture notes in artificial intelligence. Springer, Berlin, pp 27-34
57. Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
58. AK Qin VL Huang PN Suganthan 2009 Differential evolution algorithm with strategy adaptation for global numerical optimization IEEE Trans Evol Comput 13 2 298 417 10.1109/TEVC.2008.927706
59. Rego C, Alidaee B (2005) Metaheuristic optimization via memory and evolution, Tabu search and scatter search. Springer, Berlin
60. Rosin CD, Halliday R Scott, Hart WE, Belew RK (1997) A comparison of global and local search methods in drug docking. In: Bäck T (ed) Proceeding of the seventh international conference on genetic algorithms (ICGA97). Morgan Kaufmann, San Francisco, pp 221-228
61. Siarry P, Michalewicz Z (2007) Advances in metaheuristics for hard optimization. Natural computing series. Springer, Berlin
62. K Socha M Dorigo 2008 Ant colony optimization for continuous domains Eur J Oper Res 185 3 1155 1173 2361750 1146.90537 10.1016/j.ejor.2006.06.046 (Pubitemid 47505285)
63. FJ Solis RJB Wets 1981 Minimization by random search techniques, Mathematical Oper Res 6 19 30 618961 0502.90070
64. JC Spall 1992 Multivariate stochastic approximation using a simulation perturbation gradient approximation IEEE Trans Autom Control 37 332 341 1148715 0745.60110 10.1109/9.119632
65. JC Spall 1998 Implementation of the simultaneous perturbation algorithm for stochastic optimization IEEE Trans Aerosp Electron Syst 34 817 823 10.1109/7.705889 (Pubitemid 128737755)
66. Torezon VJ (1989) Multi-directional search, a direct search algorithm for parallel machines. PhD Thesis, Rice University
67. Vrugt JA, Robinson BA, Hyman JM (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2)
68. Y-J Wang J-S Zhang 2007 An efficient algorithm for large scale global optimization of continuous functions J Comput Appl Math 206 1015 1026 2333729 1117.65095 10.1016/j.cam.2006.09.006 (Pubitemid 46856055)
69. Wright MH (1996) Direct search methods, Once Scorned, now respectable. In: Griffiths DF, Watson GA (eds) Numerical analysis, 1995. Addison-Wesley Longman, Harlow, pp 191-208
70. Z Yang K Tang X Yao 2008 Large scale evolutionary optimization using cooperative coevolution Inf Sci 178 2986 2999 2433244
71. X Yao Y Liu G Lin 1999 Evolutionary programming made faster IEEE Trans Evol Comput 3 2 82 102 10.1109/4235.771163
72. W Zhong J Liu M Xue L Jiao 2004 A Multiagent genetic algorithm for global numerical optimization IEEE Trans Syst Man Cybern, Part B, Cybern 34 2 1128 1141 10.1109/TSMCB.2003.821456
73. J Zilinskas 2008 Branch and bound with simplicial partitions for global optimization Math Model Anal 13 1 145 159 2400145 1146.49029 10.3846/1392-6292.2008.13.145-159