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.
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Aarts EHL, Lenstra JK (2003) Local Search in Combinatorial Optimization. Princeton University Press, Princeton
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
Ahuja RK, Orlin JB (1996) Use of representative operation counts in computational testing of algorithms. INFORMS J Comput 8(3):318–330
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
Aldefeld B (1988) Variation of geometrics based on a geometric-reasoning method. Comput Aided Des 20(3):117–126
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
Bianchi L, DorigoM, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatorial optimization. Nat Comput 8(2):239–287
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
Blum C, Dorigo M (2004) The hyper-cube framework for ant colony optimization. IEEE Trans Syst Man Cybern - Part B 34(2):1161–1172
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput Surv 35(3):268–308
Borcea C, Streinu I (2002) On the number of embeddings of minimally rigid graphs. SoCG’02
Bouma W, Fudos I, Hoffman C, Cai J, Paige R (1995) Geometric constraint solver. Comput Aided Des 27(6):487–501
Brüderlin BD (1990) Symbolic computer geometry for computer aided geometric design. In: Advances in design and manufacturing systems. Proceedings NSF Conference, Tempe
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
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
Chunhong C (2004) The application of crossbreeding particle swarm optimizer in the engineering geometric constraint solving. Chin J Sci Instrum 29(8):397–400
Chunhong C (2004) Improved ant colony algorithm applied in constraint solving. J Eng Graph 4(4):46–50
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
Deb K (2005) Multi-objective optimization. In: Burke EK and Kendall G (eds) Search methodologies. Springer, pp 273–316
Devore JL (2004) Probability and statistics for engineering and the sciences, 6th. Duxburg and Brooks Cole, Pacific Grove
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
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press
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
Eshelman LJ (1991) The CHC adaptative search algorithm: how to safe search when engaging in nontraditional genetic recombination. Found Genet Algoritm I 265–283
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
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
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
Fudos I, Hoffmann CM (1997) A graph-constructive approach to solving systems of geometric constraints. ACM Trans Graph 16(2):179–216
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
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco
Ge J-X, Chou S-C, Gao X-S (1999) Geometric constraint satisfaction using optimization methods. Comput Aided Des 31(14):867–879
Glover F, Laguna M (1993) Tabu search. In: Reeves C (ed) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publishing, Oxford
Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Second international conference on genetic algorithms. pp 41–49
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
Harik GR (1999) Linkage learning via probabilistic modeling in the ECGA. Technical Report 99010. University of Illinois, Illinois
Harik GR, Lobo FG, Goldberg DE (1999) The compact genetic algorithm. IEEE Trans Evol Comput 3(2):287–297
Hedar A-R, Ali AF (2012) Tabu search with multi-level neighborhood structures for high dimensional problems. Appl Intell 37(2):189–206
Hidalgo MR, Joan-Arinyo R (2014) The reachability problem in constructive geometric constraint solving based dynamic geometry. J Autom Reason 52(1):99–122
Hoffmann CM, O’Donnell MJ (1982) Pattern matching in trees. J ACM 29(1):68–95
Hoffmann CM, Sitharam M, Yuan B (2004) Making constraint solvers more usable: overconstraint problem. Comput-Aided Des 36(4):377–399
Hoffmann CM, Joan-Arinyo R (2005) A brief on constraint solving. Comput-Aided Des Appl 2(5):655–663
Hoffmann CM, Lomonosov A, SitharamM(2001) Decomposition plans for geometric constraint. problems, part II: new algorithms. J Symb Comput 31:409–427
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
Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge
Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70
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
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
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
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
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
Joan-Arinyo R, Soto-Riera A (1999) Combining constructive and equational geometric constraint solving techniques. ACM Trans Graph 18(1):35–55
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
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
Joan-Arinyo R, Luzón MV, Soto-Riera A (2002) Constructive geometric constraint solving: A new application of genetic algorithms. In: PPSN. pp 759–768
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
De Jong KA (2006) Evolutionary Computation: a unified approach. MIT Press, Cambridge
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
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
Larrañaga P, Lozano JA (2002) Estimation of distribution algorithms: a new tool for evolutionary computation. Springer
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
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)
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
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
Martí R (2003) Handbook of metaheuristics, chapter multi start methods. Kluwer Academic Publishers, pp 355–368
Mata N (2000) Constructible geometric problems with interval parameters. PhD thesis, Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya Barcelona, Spain
Miller GL, Ramachandran V (1992) A new graph triconnectivity algorithm and its parallelization. Combinatorica 12(1):53–76
Mladenović N, Hansen P (2001) Variable neighborhood search: Principles and applications. Eur J Oper Res 130:449–467
Oei CK, Goldberg DE, Chang SJ (1991) Tournament selection, niching and the preservation of diversity. Technical Report 91011. University of Illinois, Illinois
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
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
Pètrowski A (1996) Clearing procedure as a niching method for genetic algorithms. In: IEEE International Conference on Evolutionary Computation. Nagoya, pp 798–803
Ramalhino H, Martin O, Stützle T (2002) Iterated local search. In: Glover F, Kochenberger G (eds) Handbook of Metaheuristics, pp 321–353
Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edition. Prentice Hall
Sareni B, Krähenbühl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106
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
SolBCN GCS (2011) Solbcn a constraint-based two dimensional geometric editor. Geometric Constraint Solving Group of the Universitat Politècnica de Catalunya
Song W, Liu Y, Li J (2014) Mining high utility itemsets by dynamically pruning the tree structure. Appl Intell 40(1):29–43
Stützle T, Hoos HH (2000) Max-min ant system. Future Gener Comput Syst 16(8):889–914
Thierens D Thierens D (2004) Population-based iterated local search: restricting neighborhood search by crossover. Lect Notes Comput Sci 3103/2004:234–245
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
Wu Y, Wang L, Ren J, Ding W, Wu X (2014) Mining sequential patterns with periodic wildcard gaps. Appl Intell:1–18
Yeguas E (2011) Benchmark for the root identification problem in geometric constraint solving. Comput Aided Des. URL http://www.uco.es/in1yeboe/benchmark.html
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
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
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
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
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.
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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
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DOI: https://doi.org/10.1007/s10489-014-0560-y