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Harmony search based memetic algorithms for solving sudoku

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Abstract

The development of hybrid procedures for optimization focuses on enhancing the strength and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of the competing methodologies to create a superior solution procedure. The objective of this paper is to explore the hybridization between Harmony Search and Hill Climbing algorithm by utilizing the exploration power of the former and exploitation power of the latter in the context of solving Sudoku which is a well-known hard combinatorial optimization problem. We call this hybrid algorithm Harmony Search Hill Climber (HSHC). In order to extend the exploration capabilities of HSHC it is further modified to create three different algorithms namely Retrievable Harmony Search Hill Climber (RHSHC), Global Best Retrievable Harmony Search Hill Climber (GB-RHSHC) and Random Best Retrievable Harmony Search Hill Climber (RB-RHSHC). Comparing the four algorithms proposed in this paper RHSHC outperforms its three variations in terms of effectiveness. Experimental results demonstrate that RHSHC perform significantly better than standard Harmony Search algorithm and standard Hill climber algorithm. On comparing RHSHC with the genetic algorithm it has been concluded that former outperforms latter both in terms of effectiveness and efficiency particularly for Hard and Expert level puzzles. Comparing RHSHC and hybrid AC3-tabu search algorithm it has been concluded that RHSHC is very competent to hybrid AC3-tabu search algorithm.

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References

  • Takayuki Y, Takahiro S (2003) Complexity and completeness of finding another solution and its application to puzzles. IEICE Trans Fundam Electron Commun Comput Sci 86(5):1052–1060

    Google Scholar 

  • Mantere T, Koljonen J (2006) Solving and rating sudoku puzzles with geneticalgorithms. In: New developments in artificial intelligence and the semantic web, proceedings of the 12th finnish artificial intelligence conference STeP. Citeseer, 2006, pp 86–92

  • Jones SK, Roach PA, Perkins S (2008) Construction of heuristics for a search-based approach to olving sudoku. In: Research and development in intelligent systems XXIV. Springer, 2008, pp. 37–49

  • Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Gholizadeh S, Barzegar A (2013) Shape optimization of structures for frequency constraints by sequential harmony search algorithm. Eng Optim 45(6):627–646

    Article  MathSciNet  Google Scholar 

  • Wang L, Li L-P (2013) An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. Int J Electr Power Energy Syst 44(1):832–843

    Article  Google Scholar 

  • Nekooei K, Farsangi MM, Nezamabadi-Pour H, Lee KY (2013) An improved multi-objective harmony search for optimal placement of dgs in distribution systems. Smart Grid IEEE Trans 4(1):557–567

    Article  Google Scholar 

  • Hadwan M, Ayob M, Sabar NR, Qu R (2013) A harmony search algorithm for nurse rostering problems. Inf Sci 233:126–140

    Article  MathSciNet  Google Scholar 

  • Diao R, Shen Q (2012) Feature selection with harmony search. Syst Man Cybern Part B Cybern IEEE Trans 42(6):1509–1523

    Article  Google Scholar 

  • Fattahi H, Gholami A, Amiribakhtiar MS, Moradi S (2015) Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search. Neural Comput Appl 26(4):789–798

    Article  Google Scholar 

  • Al-Betar MA, Khader AT, Zaman M (2012) University course timetabling using a hybrid harmony search metaheuristic algorithm. Syst Man Cybern Part C Appl Rev IEEE Trans 42(5):664–681

    Article  Google Scholar 

  • Geem ZW (2005) Harmony search in water pump switching problem. In: Proceedings of international conference on natural computation. Springer, pp. 751–760

  • Ong Y-S, Lim M-H, Zhu N, Wong K-W (2006) Classification of adaptive memetic algorithms: a comparative study. Syst Man Cybern Part B Cybern IEEE Trans 36(1):141–152

    Article  Google Scholar 

  • Dawkins R (2006) The selfish gene. Oxford university press, no. 199

  • Ong Y-S, Nguyen Q-H, Lim M-H, Jing T (2006) A development platform for memetic algorithm design. In: SCIS and ISIS 2006. Japan society for fuzzy theory and intelligent informatics, pp 1027–1032

  • Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. Evolut Comput IEEE Trans 7(2):204–223

    Article  Google Scholar 

  • Chan T-M, Leung K-S, Lee K-H (2012) Memetic algorithms for de novo motif discovery. Evolut Comput IEEE Trans 16(5):730–748

    Article  Google Scholar 

  • Sharma H, Bansal JC, Arya KV, Yang X-S (2016) Lévy flight artificial bee colony algorithm. Int J Syst Sci 47(11):2652–2670

    Article  MATH  Google Scholar 

  • Jadon SS, Bansal JC, Tiwari R, Sharma H (2015) Accelerating artificial bee colony algorithm with adaptive local search. Memet Comput 7(3):215–230

    Article  Google Scholar 

  • Sharma H, Bansal JC, Arya K (2013) Power law-based local search in differential evolution. Int J Comput Intell Stud 2(2):90–112

    Article  Google Scholar 

  • Hart WE, Krasnogor N, Smith JE (2005) Recent advances in memetic algorithms, vol 166. Springer Science & Business Media, New York

    Book  MATH  Google Scholar 

  • Moon TK, Gunther JH (2006) Multiple constraint satisfaction by belief ropagation: an example using sudoku. In: IEEE mountain workshop on 2006. Adaptive and learning systems, IEEE, 2006, pp. 122–126

  • Lynce I, Ouaknine J (2006) Sudoku as a sat problem. In: Proceedings of the 9th Symposium on Artificial Intelligence and Mathematics (AIMATH), 6 jan 2006

  • Lewis R (2007) Metaheuristics can solve sudoku puzzles. J Heuristics 13(4):387–401

    Article  Google Scholar 

  • Mullaney D (2006) Using ant systems to solve sudoku problems. University College Dublin, Dublin

    Google Scholar 

  • Boryczka U, Juszczuk P (2012) Solving the sudoku with the differential evolution. Zeszyty Naukowe Politechniki Białostockiej. Informatyka, pp 5–16

  • Moon TK, Gunther JH, Kupin JJ (2009) Sinkhorn solves sudoku. Inf Theory IEEE Trans 55(4):1741–1746

    Article  MathSciNet  MATH  Google Scholar 

  • Gunther J, Moon T (2012) Entropy minimization for solving sudoku. Signal Process IEEE Trans 60(1):508–513

    Article  MathSciNet  MATH  Google Scholar 

  • Garey M, Johnson D (1979) Computers and intractability WH freeman and company New York

  • Das KN, Bhatia S, Puri S, Deep K (2012) A retrievable ga for solving sudoku puzzles. Tech. Rep, Citeseer

  • Nicolau M, Ryan C (2006) Solving sudoku with the gauge system. In: Genetic programming. Springer, pp 213–224

  • Li Y, Deng X (2011) Solving sudoku puzzles based on improved genetic algorithm. Jisuanji Yingyong Yu Ruanjian 28(3):68–70

    Google Scholar 

  • Sato Y, Inoue H (2010) Solving sudoku with genetic operations that preserve building blocks. In: IEEE Symposium on Computational intelligence and games (CIG), 2010. IEEE, pp 23–29

  • Deng XQ, Da Li Y (2013) A novel hybrid genetic algorithm for solving sudoku puzzles. Optim Lett 7(2):241–257

    Article  MathSciNet  MATH  Google Scholar 

  • Sato Y, Hasegawa N, Sato M (2013) Acceleration of genetic algorithms for sudoku solution on many-core rocessors. In: Massively parallel evolutionary computation on GPGPUs. Springer, pp 421–444

  • Moraglio A, Togelius J, Lucas S (2006) Product geometric crossover for the sudoku puzzle. In: IEEE congress on evolutionary computation, 2006. CEC 2006. IEEE, pp 470–476

  • Soto R, Crawford B, Galleguillos C, Paredes F, Norero E, (2015) A hybrid alldifferent-tabu search algorithm for solving sudoku puzzles. Comput Intell Neurosci 2015

  • Wang Z, Yasuda T, Ohkura K, (2015) An evolutionary approach to sudoku puzzles with filtered mutations. In: IEEE congress on evolutionary computation (CEC), 2015. IEEE, pp 1732–1737

  • Soto R, Crawford B, Galleguillos C, Monfroy E, Paredes F (2013) A hybrid ac3-tabu search algorithm for solving sudoku puzzles. Exp Syst Appl 40(15):5817–5821

    Article  Google Scholar 

  • Simonis H (2005) Sudoku as a constraint problem. In: CP workshop on modeling and reformulating constraint satisfaction problems. Citeseer, vol 12, pp 13–27

  • Rossi F, Van Beek P, Walsh T (2006) Handbook of constraint programming. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Manter T, Koljonen J (2007) Solving, rating and generating sudoku puzzles with ga. In: IEEE Congress on evolutionary computation, CEC 2007. IEEE 2007, pp 1382–1389

  • Soto R, Crawford B, Galleguillos C, Monfroy E, Paredes F (2014) A prefiltered cuckoo search algorithm with geometric operators for solving sudoku problems, vol 2014. The Scientific World Journal

  • Geem ZW (2007) Harmony search algorithm for solving sudoku. In: Knowledge-based intelligent information and engineering systems. Springer, pp 371–378

  • Weyland D (2015) A critical analysis of the harmony search algorithm how not to solve sudoku. Op Res Perspect 2:97–105

    MathSciNet  Google Scholar 

  • Durstenfeld R (1964) Algorithm 235: random permutation. Commun ACM 7(7):420

    Article  Google Scholar 

  • Jin X, Li Z (1997) Genetic-catastrophic algorithms and its application in nonlinear control system. J Syst Simul 9(2):111–115

    Google Scholar 

Download references

Acknowledgements

The first author would like to acknowledge QIP Centre Indian Institute of Technology Roorkee, India and All India Council for Technical Education (AICTE) for sponsoring his research.

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Correspondence to Assif Assad.

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Assad, A., Deep, K. Harmony search based memetic algorithms for solving sudoku. Int J Syst Assur Eng Manag 9, 741–754 (2018). https://doi.org/10.1007/s13198-017-0620-x

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