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A Composite Function for Understanding Bin-Packing Problem and Tabu Search: Towards Self-adaptive Algorithms

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12949))

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Abstract

Different research problems (optimization, classification, ordering) have shown that some problem instances are better solved by a certain solution algorithm in comparison to any other. A literature review indicated implicitly that this phenomenon has been identified, formulated, and analyzed in understanding levels descriptive and predictive without obtaining a deep understanding. In this paper a formulation of phenomenon as problem in the explanatory understanding level and a composite function to solve it are proposed. Case studies for Tabu Search and One Dimension Bin Packing were conducted over set P. Features that describe problem instance (structure, space) and algorithm behavior (searching, operative) were proposed. Three algorithm logical areas were analyzed. Knowledge acquired by the composite function allowed designing of self-adaptive algorithms, which adapt the algorithm logic according to the problem instance description in execution time. The new, self-adaptive algorithms have a statistically significant advantage to the original algorithm in an average 91% of problem instances; other results (set P’) indicate that when they obtain a best solution quality, it is significant and when they obtain the same or less solution quality, they finish significantly faster than original algorithm. The composite function can be a viable methodology toward the search of theories that permit the design of self-adaptive algorithms, solving real problems optimally.

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References

  1. Lagoudakis, M., Littman, M.: Learning to select branching rules in the DPLL procedure for satisfiability. Electron. Notes Discrete Math. 9, 344–359 (2001)

    Article  Google Scholar 

  2. Chr, P., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity (1982)

    Google Scholar 

  3. Flum, J., Grohe, M.: Parameterized Complexity Theory. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-29953-X

    Book  MATH  Google Scholar 

  4. Rendell, L., Cho, H.: Empirical learning as a function of concept character. Mach. Learn. 5, 267–298 (1990)

    Google Scholar 

  5. Cohen, P.: Empirical Methods for Artificial Intelligence. The MIT Press, Cambridge (1995)

    MATH  Google Scholar 

  6. Barr, R., Golden, B., Kelly, J., Resende, M.: Designing and reporting on computational experiments with heuristic methods. J. Heuristics 1(1), 9–32 (1995)

    Article  Google Scholar 

  7. Wolpert, D., Macready, W.: No free lunch theorems for optimizations. IEEE Trans. Evol. Comput. 1(1), 67–82 (1996)

    Article  Google Scholar 

  8. Frost, D., Dechter, R.: In search of the best constraint satisfaction search. In: Proceedings of the National Conference on Artificial Intelligence, Seattle, vol. 94, pp. 301–306 (1994)

    Google Scholar 

  9. Tsang, E., Borrett, J., Kwan, A. An attempt to map the performance of a range of algorithm and heuristic combinations. In: Hallam, J., et al. (eds.) Hybrid Problems, Hybrid Solutions. Proceedings of AISB-95, vol. 27, pp. 203–216. IOS Press, Amsterdam (1995)

    Google Scholar 

  10. Frost, D., Rish, I., Vila, L.: Summarizing CSP hardness with continuous probability distributions. In: Proceedings of the 14th National Conference on AI, American Association for Artificial Intelligence, pp. 327–333 (1997)

    Google Scholar 

  11. Vanchipura, R., Sridharan, R.: Development and analysis of constructive heuristic algorithms for flow shop scheduling problems with sequence-dependent setup times. Int. J. Adv. Manufact. Technol. 67, 1337–1353 (2013)

    Article  Google Scholar 

  12. Rice, J.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)

    Article  Google Scholar 

  13. Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 1–25 (2009)

    Article  Google Scholar 

  14. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the empirical hardness of optimization problems: the case of combinatorial auctions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 556–572. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46135-3_37

    Chapter  Google Scholar 

  15. Silverthorn, B., Miikkulainen, R.: Latent class models for algorithm portfolio methods. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, Georgia, USA (2010)

    Google Scholar 

  16. Yuen, S., Zhang, X.: Multiobjective evolutionary algorithm portfolio: choosing suitable algorithm for multiobjective optimization problem. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, pp. 1967–1973 (2014)

    Google Scholar 

  17. Guerri, A., Milano, M.: Learning techniques for automatic algorithm portfolio selection. In: Proceedings of the 16th Biennial European Conference on Artificial Intelligence, Valencia, Spain, pp. 475–479. IOS Press, Burke (2004)

    Google Scholar 

  18. Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: Proceedings of the 25th National Conference on Artificial Intelligence (AAAI 2010), pp. 210–216 (2010)

    Google Scholar 

  19. Pavón, R., Díaz, F., Laza, R., Luzón, M.: Experimental evaluation of an automatic parameter setting system. Expert Syst. Appl. 37, 5224–5238 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Pérez, J., Pazos, R.A., Frausto, J., Rodríguez, G., Romero, D., Cruz, L.: A statistical approach for algorithm selection. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 417–431. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24838-5_31

    Chapter  Google Scholar 

  22. Ries, J., Beullens, P.: A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction. J. Oper. Res. Soc. 66(5), 782–793 (2015)

    Article  Google Scholar 

  23. Smith-Miles, K., van Hemert, J., Lim, X.Y.: Understanding TSP difficulty by learning from evolved instances. In: Blum, C., Battiti, R. (eds.) LION 2010. LNCS, vol. 6073, pp. 266–280. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13800-3_29

    Chapter  Google Scholar 

  24. Hutter, F., Xu, L., Hoos, H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)

    Article  MathSciNet  Google Scholar 

  25. Leyton-Brown, K., Hoos, H., Hutter, F., Xu, L.: Understanding the empirical hardness of NP-complete problems. Mag. Commun. ACM 57(5), 98–107 (2014)

    Article  Google Scholar 

  26. Munoz, M., Kirley, M., Halgamuge, S.: Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. 19(1), 74–87 (2015)

    Article  Google Scholar 

  27. Kottho, L., Gent, I.P., Miguel, I.: An evaluation of machine learning in algorithm selection for search problems. AI Commun. 25(3), 257–270 (2012)

    Article  MathSciNet  Google Scholar 

  28. Lopez, T.T., Schaeer, E., Domiguez-Diaz, D., Dominguez-Carrillo, G.: Structural effects in algorithm performance: a framework and a case study on graph coloring. In: Computing Conference, 2017, pp. 101–112. IEEE (2017)

    Google Scholar 

  29. Fu, H., Xu, Y., Chen, S., Liu, J.: Improving WalkSAT for random 3-SAT problems. J. Univ. Comput. Sci. 26(2), 220–243 (2020)

    MathSciNet  Google Scholar 

  30. Tavares, J.: Multidimensional knapsack problem: a fitness landscape analysis. IEEE Trans. Syst. Man Cybern. Part B: Cynern. 38(3), 604–616 (2008)

    Article  Google Scholar 

  31. Watson, J., Darrell, W., Adele, E.: Linking search space structure, run-time dynamics, and problem difficulty: a step toward demystifying tabu search. J. Artif. Intell. Res. 24, 221–261 (2005)

    Article  Google Scholar 

  32. Watson, J.: An introduction to fitness landscape analysis and cost models for local search. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 599–623. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1665-5_20

  33. Chevalier, R.: Balancing the effects of parameter settings on a genetic algorithm for multiple fault diagnosis. Artificial Intelligence, University of Georgia (2006)

    Google Scholar 

  34. Cayci, A., Menasalvas, E., Saygin, Y., Eibe, S.: Self-configuring data mining for ubiquitous computing. Inf. Sci. 246, 83–99 (2013)

    Article  Google Scholar 

  35. Le, M., Ong, Y., Jin, Y.: Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memetic Comput. 1, 175–190 (2009)

    Article  Google Scholar 

  36. Montero, E., Riff, M.: On-the-fly calibrating strategies for evolutionary algorithms. Inf. Sci. 181, 552–566 (2011)

    Article  Google Scholar 

  37. Pérez, J., Cruz, L., Landero, V.: Explaining performance of the threshold accepting algorithm for the bin packing problem: a causal approach. Pol. J. Environ. Stud. 16(5B), 72–76 (2007)

    Google Scholar 

  38. Pérez, J., et al.: An application of causality for representing and providing formal explanations about the behavior of the threshold accepting algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1087–1098. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69731-2_102

    Chapter  Google Scholar 

  39. Quiroz-Castellanos, M., Cruz-Reyes, L., Torres-Jimenez, J., Gómez, C., Huacuja, H.J.F., Alvim, A.C.: A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Comput. Oper. Res. 55, 52–64 (2015)

    Article  MathSciNet  Google Scholar 

  40. Taghavi, T., Pimentel, A., Sabeghi, M.: VMODEX: a novel visualization tool for rapid analysis of heuristic-based multi-objective design space exploration of heterogeneous MPSoC architectures. Simul. Model. Pract. Theory 22, 166–196 (2012)

    Article  Google Scholar 

  41. Landero, V., Pérez, J., Cruz, L., Turrubiates, T., Ríos, D.: Effects in the algorithm performance from problem structure, searching behavior and temperature: a causal study case for threshold accepting and bin-packing. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11619, pp. 152–166. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24289-3_13

    Chapter  Google Scholar 

  42. Landero, V., Ríos, D., Pérez, J., Cruz, L., Collazos-Morales, C.: Characterizing and analyzing the relation between bin-packing problem and tabu search algorithm. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12249, pp. 149–164. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58799-4_11

    Chapter  Google Scholar 

  43. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. The MIT Press, Cambridge (2001)

    Book  Google Scholar 

  44. Beasley, J.E.: OR-Library. Brunel University (2006). http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html

  45. Scholl, A., Klein, R. (2003). http://www.wiwi.uni-jena.de/Entscheidung/binpp/

  46. Glover, F.: Tabu search - Part I, first comprehensive description of tabu search. ORSA-J. Comput. 1(3), 190–206 (1989)

    Article  Google Scholar 

  47. Fleszar, K., Hindi, K.S.: New heuristics for one-dimensional bin packing. Comput. Oper. Res. 29, 821–839 (2002)

    Article  Google Scholar 

  48. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)

    Google Scholar 

  49. Merz, P., Freisleben, B.: Fitness landscapes and memetic algorithm design. In: New Ideas in Optimization, pp. 245–260. McGraw-Hill Ltd., UK (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Apodaca, Ciudad Apodaca, Nuevo León, México

    V. Landero & David Ríos

  2. Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET), Departamento de Ciencias Computacionales, AP 5-164, 62490, Cuernavaca, México

    O. Joaquín Pérez

  3. Universidad Manuela Beltrán, Bogotá, Colombia

    Carlos Andrés Collazos-Morales

Authors
  1. V. Landero
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  2. David Ríos
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  3. O. Joaquín Pérez
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  4. Carlos Andrés Collazos-Morales
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Corresponding author

Correspondence to V. Landero .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Landero, V., Ríos, D., Pérez, O.J., Collazos-Morales, C.A. (2021). A Composite Function for Understanding Bin-Packing Problem and Tabu Search: Towards Self-adaptive Algorithms. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12949. Springer, Cham. https://doi.org/10.1007/978-3-030-86653-2_43

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  • DOI: https://doi.org/10.1007/978-3-030-86653-2_43

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