Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Conflict-based pruning of a solution space within a constructive geometric constraint solver

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The Computer-Aided Design field has developed sketching systems that automatically instantiate geometric objects from a rough sketch, annotated with dimensions and constraints input by the user. Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. The user is only interested in the intended solution that, besides fulfilling the geometric constraints, exhibits some additional properties. Metaheuristics have been successfully applied to solve this problem named as Root Identification Problem. However, these methods are very time-consuming because of the time required to evaluate every candidate solution. Pruning the search space is paramount to simplify the number of solution instances evaluated before finding the intended solution. In this work, we present an algorithm for pruning based on the detection of conflicts, i.e. patterns that drive to non-feasible solutions. Subsequent solutions will not be evaluated in case of matching a neighborhood corresponding to a previously detected conflicting pattern. The algorithm may be integrated in the evaluation phase of techniques that dynamically explore the search space, like metaheuristics, significantly improving the required computational time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Aarts EHL, Lenstra JK (2003) Local Search in Combinatorial Optimization. Princeton University Press, Princeton

    MATH  Google Scholar 

  2. Ahmed CF, Tanbeer SK, Jeong B-S, Lee Y-K (2011) HUC-Prune: an efficient candidate pruning technique to mine high utility patterns. Appl Intell 34(2):181–198

    Article  Google Scholar 

  3. Ahuja RK, Orlin JB (1996) Use of representative operation counts in computational testing of algorithms. INFORMS J Comput 8(3):318–330

    Article  MATH  MathSciNet  Google Scholar 

  4. Ait-Aoudia S, Bahriz M, Salhi L (2009) 2d geometric constraint solving: An overview. In: Proceedings of the 2009 Second international conference in visualisation. VIZ ’09. IEEE Computer Society, Washington, pp 201–206

    Chapter  Google Scholar 

  5. Aldefeld B (1988) Variation of geometrics based on a geometric-reasoning method. Comput Aided Des 20(3):117–126

    Article  MATH  Google Scholar 

  6. Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  7. Bianchi L, DorigoM, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatorial optimization. Nat Comput 8(2):239–287

    Article  MATH  MathSciNet  Google Scholar 

  8. Birattari M, Paquete L, Sttzle T, Varrentrapp K (2001) Classification of metaheuristics and design of experiments for the analysis of components. Technical Report AIDA-01-05, Darmstadt University of Technology

  9. Blum C, Dorigo M (2004) The hyper-cube framework for ant colony optimization. IEEE Trans Syst Man Cybern - Part B 34(2):1161–1172

    Article  Google Scholar 

  10. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  11. Borcea C, Streinu I (2002) On the number of embeddings of minimally rigid graphs. SoCG’02

  12. Bouma W, Fudos I, Hoffman C, Cai J, Paige R (1995) Geometric constraint solver. Comput Aided Des 27(6):487–501

    Article  MATH  Google Scholar 

  13. Brüderlin BD (1990) Symbolic computer geometry for computer aided geometric design. In: Advances in design and manufacturing systems. Proceedings NSF Conference, Tempe

  14. Bullnheimer B, Hartl R F, Strauβ C (1999) A new rank based version of the ant system: a computational study. Cent Eur J Oper Res Econ 1(7):25–38

    Google Scholar 

  15. Cedeño W, Vemuri V R, Slezak T (1994) Multiniche crowding in genetic algorithms and its application to the assembly of dna restriction-fragments. Evol Comput 2:321–345

    Article  Google Scholar 

  16. Chunhong C (2004) The application of crossbreeding particle swarm optimizer in the engineering geometric constraint solving. Chin J Sci Instrum 29(8):397–400

    Google Scholar 

  17. Chunhong C (2004) Improved ant colony algorithm applied in constraint solving. J Eng Graph 4(4):46–50

    Google Scholar 

  18. Cordón O, Fernández de Viana I, Herrera F (2002) Analysis of the best-worst ant system and its variants on the QAP. In: ANTS ’02: Proceedings of the third international workshop on ant algorithms. Springer-Verlag, London, pp 228–234

    Google Scholar 

  19. Deb K (2005) Multi-objective optimization. In: Burke EK and Kendall G (eds) Search methodologies. Springer, pp 273–316

  20. Devore JL (2004) Probability and statistics for engineering and the sciences, 6th. Duxburg and Brooks Cole, Pacific Grove

    Google Scholar 

  21. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern - Part B 26(1):29–41

    Article  Google Scholar 

  22. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press

  23. Eiben AE, Ruttkay Zs (1997) Constraint-satisfaction problems. In: Bäck T, Fogel D andMichalewicz Z (eds) Handbook of evolutionary computation. Institute of Physics Publishing Ltd and Oxford University Press, pp C5.7:1–C5.7:5

  24. Eshelman LJ (1991) The CHC adaptative search algorithm: how to safe search when engaging in nontraditional genetic recombination. Found Genet Algoritm I 265–283

  25. Essert-Villard C, Schreck P, Dufourd J-F (2000) Sketch-based pruning of a solution space within a formal geometric constraint solver. Artif Intell 124:139–159

    Article  MATH  MathSciNet  Google Scholar 

  26. Freixas M, Joan-Arinyo R, Soto-Riera A (2008) A constraint-based dynamic geometry system. In: SPM ’08: Proceedings of the 2008 ACM symposium on Solid and physical modeling. ACM, New York, pp 37–46

    Chapter  Google Scholar 

  27. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  Google Scholar 

  28. Fudos I, Hoffmann CM (1997) A graph-constructive approach to solving systems of geometric constraints. ACM Trans Graph 16(2):179–216

    Article  Google Scholar 

  29. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and datamining: Experimental analysis of power. Inf Sci 180(10):2044–2064

    Article  Google Scholar 

  30. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco

    MATH  Google Scholar 

  31. Ge J-X, Chou S-C, Gao X-S (1999) Geometric constraint satisfaction using optimization methods. Comput Aided Des 31(14):867–879

    Article  MATH  Google Scholar 

  32. Glover F, Laguna M (1993) Tabu search. In: Reeves C (ed) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publishing, Oxford

    Google Scholar 

  33. Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Second international conference on genetic algorithms. pp 41–49

  34. Guo D, Hu X, Xie F, Wu X (2013) Pattern matching with wildcards and gap-length constraints based on a centrality-degree graph. Appl Intell 39(1):57–74

    Article  Google Scholar 

  35. Harik GR (1999) Linkage learning via probabilistic modeling in the ECGA. Technical Report 99010. University of Illinois, Illinois

    Google Scholar 

  36. Harik GR, Lobo FG, Goldberg DE (1999) The compact genetic algorithm. IEEE Trans Evol Comput 3(2):287–297

    Article  Google Scholar 

  37. Hedar A-R, Ali AF (2012) Tabu search with multi-level neighborhood structures for high dimensional problems. Appl Intell 37(2):189–206

    Article  Google Scholar 

  38. Hidalgo MR, Joan-Arinyo R (2014) The reachability problem in constructive geometric constraint solving based dynamic geometry. J Autom Reason 52(1):99–122

    Article  MathSciNet  Google Scholar 

  39. Hoffmann CM, O’Donnell MJ (1982) Pattern matching in trees. J ACM 29(1):68–95

    Article  MATH  MathSciNet  Google Scholar 

  40. Hoffmann CM, Sitharam M, Yuan B (2004) Making constraint solvers more usable: overconstraint problem. Comput-Aided Des 36(4):377–399

    Article  Google Scholar 

  41. Hoffmann CM, Joan-Arinyo R (2005) A brief on constraint solving. Comput-Aided Des Appl 2(5):655–663

    Google Scholar 

  42. Hoffmann CM, Lomonosov A, SitharamM(2001) Decomposition plans for geometric constraint. problems, part II: new algorithms. J Symb Comput 31:409–427

    Article  MathSciNet  Google Scholar 

  43. Hoffmann CM, Lomonosov A, Sitharam M (2001) Decomposition plans for geometric constraint. systems, part I: performance measurements for CAD. J Symb Comput 31:367–408

    Article  MathSciNet  Google Scholar 

  44. Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Google Scholar 

  45. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MATH  MathSciNet  Google Scholar 

  46. Hoos HH, Stützle T (1998) Evaluating Las Vegas algorithms Pitfalls and remedies. In: Proceedings of the 14th conference on uncertainly in artificial intelligence. Morgan Kaufmann, pp 238–245

  47. Jermann C, Trombettoni G, Neveu B, Mathis P (2006) Decomposition of geometric constraint systems: a survey. Int J Comput Geometry Appl 16(5–6):379–414

    Article  MATH  MathSciNet  Google Scholar 

  48. Joan-Arinyo R, Luzón MV, Yeguas E (2008) Parameter tuning for PBIL algorithm in geometric constraint solving systems. In: World congress in computer science, Computer Engineering and Applied Computing. International Conference on Genetics and Evolutionary Methods. pp 69–75

  49. Joan-Arinyo R, Luzon MV, Yeguas E (2009) Search space pruning to solve the root identification problem in geometric constraint solving. Comput-Aided Des Appl 6(1):15–25

    Google Scholar 

  50. Joan-Arinyo R, Luzón MV, Yeguas E (2011) Parameter tuning of pbil and chc evolutionary algorithms applied to solve the root identification problem. Appl Soft Comput 11:754–767

    Article  Google Scholar 

  51. Joan-Arinyo R, Soto-Riera A (1999) Combining constructive and equational geometric constraint solving techniques. ACM Trans Graph 18(1):35–55

    Article  Google Scholar 

  52. Joan-Arinyo R, Soto-Riera A, Vila-Marta S, Vilaplana J (2001) On the domain of constructive geometric constraint solving techniques. In: in SCCG’01: Proceedings of the 17th Spring conference on Computer graphics. pp 49–54

  53. Joan-Arinyo R, Soto-Riera A, Vila-Marta S, Vilaplana-Pastó J (2003) Transforming an under-constrained geometric constraint problem into a well-constrained one. In: Proceedings of the eighth ACM symposium on Solid modeling and applications, SM ’03. ACM, New York, pp 33–44

    Chapter  Google Scholar 

  54. Joan-Arinyo R, Luzón MV, Soto-Riera A (2002) Constructive geometric constraint solving: A new application of genetic algorithms. In: PPSN. pp 759–768

  55. Joan-Arinyo R, Tarrés-Puertas M, Vila-Marta S (2009) Treedecomposition of geometric constraint graphs based on computing graph circuits. In: 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling. SPM ’09. ACM, New York, pp 113–122

    Chapter  Google Scholar 

  56. De Jong KA (2006) Evolutionary Computation: a unified approach. MIT Press, Cambridge

    Google Scholar 

  57. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MATH  MathSciNet  Google Scholar 

  58. Kliewer G, Tschöke S (2000) A general parallel simulated annealing library and its application in airline industry. In: 14th International Parallel and Distributed Processing Symposium (IPDPS). Cancun, Mexico pp 55–61

  59. Larrañaga P, Lozano JA (2002) Estimation of distribution algorithms: a new tool for evolutionary computation. Springer

  60. Liouane N, Saad I, Hammadi S, Borne P (2007) Ant systems and local search optimization for flexible job shop scheduling production. Int J Comput Commun Control 2(2):174–184

    Google Scholar 

  61. Lu H-T, Yang W (2000) A simple tree Pattern-Matching algorithm. In: Proceedings of the workshop on algorithms and theory of computation (ICS ’00)

  62. Luzón MV, Barreiro E, Yeguas E, Joan-Arinyo R (2004) GA and CHC two evolutionary algorithms to solve the root identification problem in geometric constraint solving. Lect Notes Comput Sci 4(3039):139–146

    Article  Google Scholar 

  63. Luzón MV, Soto A, Gálvez JF, Joan-Arinyo R (2005) Searching the solution space in constructive geometric constraint solving with genetic algorithms. Appl Intell 22:109–124

    Article  MATH  Google Scholar 

  64. Martí R (2003) Handbook of metaheuristics, chapter multi start methods. Kluwer Academic Publishers, pp 355–368

  65. Mata N (2000) Constructible geometric problems with interval parameters. PhD thesis, Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya Barcelona, Spain

  66. Miller GL, Ramachandran V (1992) A new graph triconnectivity algorithm and its parallelization. Combinatorica 12(1):53–76

    Article  MATH  MathSciNet  Google Scholar 

  67. Mladenović N, Hansen P (2001) Variable neighborhood search: Principles and applications. Eur J Oper Res 130:449–467

    Article  MATH  Google Scholar 

  68. Oei CK, Goldberg DE, Chang SJ (1991) Tournament selection, niching and the preservation of diversity. Technical Report 91011. University of Illinois, Illinois

    Google Scholar 

  69. Owen Owen JC (1991) Algebraic solution for geometry from dimensional constraints. In: Rossignac R, Turner J (eds) Symposium on solid modeling foundations and CAD/CAM applications. ACM Press, Austin, pp 397–407

  70. Pérez E, Herrera F, Hernández C (2003) Finding multiple solutions in job shop scheduling by niching genetic algorithms. J Intell Manuf 14(3–4):323–339

    Article  Google Scholar 

  71. Pètrowski A (1996) Clearing procedure as a niching method for genetic algorithms. In: IEEE International Conference on Evolutionary Computation. Nagoya, pp 798–803

  72. Ramalhino H, Martin O, Stützle T (2002) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of Metaheuristics, pp 321–353

  73. Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edition. Prentice Hall

  74. Sareni B, Krähenbühl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106

    Article  Google Scholar 

  75. Sheng-Li L, Min T, Shang-Ching C, Jin-Xiang D (2004) Solving geometric constraints with niche genetic simulated annealing algorithm. In: Computer supported cooperative work in design. Proceedings. The 8th international conference on, vol. 1. pp 679–684

  76. SolBCN GCS (2011) Solbcn a constraint-based two dimensional geometric editor. Geometric Constraint Solving Group of the Universitat Politècnica de Catalunya

  77. Song W, Liu Y, Li J (2014) Mining high utility itemsets by dynamically pruning the tree structure. Appl Intell 40(1):29–43

    Article  Google Scholar 

  78. Stützle T, Hoos HH (2000) Max-min ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  79. Thierens D Thierens D (2004) Population-based iterated local search: restricting neighborhood search by crossover. Lect Notes Comput Sci 3103/2004:234–245

    Article  Google Scholar 

  80. Vila S (2003) Contribution to geometric constraint solving in cooperative engineering. PhD thesis, Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

  81. Wu Y, Wang L, Ren J, Ding W, Wu X (2014) Mining sequential patterns with periodic wildcard gaps. Appl Intell:1–18

  82. Yeguas E (2011) Benchmark for the root identification problem in geometric constraint solving. Comput Aided Des. URL http://www.uco.es/in1yeboe/benchmark.html

  83. Yeguas E, Joan-Arinyo R, Luz´on MV (2011) Modelling the performance of evolutionary algorithms on the root identification problem: a case study with PBIL and CHC algorithms. Evol Comput 19:107–135

    Article  Google Scholar 

  84. Yeguas E, Luzón MV, Pavón R, Laza R, Arroyo G, Díaz F (2014) Automatic parameter tuning for evolutionary algorithms using a bayesian case-based reasoning system. Appl Soft Comput 18:185–195

    Article  Google Scholar 

  85. Yuan H, Li Y, Li W, Zhao K, Wang D, Yi R (2008) Combining immune with ant colony algorithm for geometric constraint solving. In: Knowledge discovery and data mining. WKDD 2008. First International Workshop on, pp 524–527

  86. Zhang Y, Liu K, Liu G, Zhao Z (2010) A concurrent-hybrid evolutionary algorithm for geometric constraint solving. In: Cai Z, Tong H, Kang Z, Liu Y (eds) Computational intelligence and intelligent systems, vol 107 of Communications in Computer and Information Science. Springer, Berlin, pp 1–10

Download references

Acknowledgments

This research has been partially supported by the University of Jaén under the project R1/12/2010/61 and by the Research Projects called “TIN2012-32952” and “BROCA”, both financed by Science and Technology Ministry of Spain and FEDER. The authors thank R. Joan-Arinyo and the GIE research team at Universitat Politécnica de Catalunya for their help in the use of SolBCN software.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. Yeguas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yeguas, E., Marín-Jiménez, M.J., Muñoz-Salinas, R. et al. Conflict-based pruning of a solution space within a constructive geometric constraint solver. Appl Intell 41, 897–922 (2014). https://doi.org/10.1007/s10489-014-0560-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0560-y

Keywords