, Volume 94, Issue 11, pp 887–914 | Cite as

An optimization algorithm inspired by social creativity systems

  • Roman Anselmo Mora-Gutiérrez
  • Javier Ramírez-Rodríguez
  • Eric Alfredo Rincón-García
  • Antonin Ponsich
  • Oscar Herrera


The need for efficient and effective optimization problem solving methods arouses nowadays the design and development of new heuristic algorithms. This paper present ideas that leads to a novel multiagent metaheuristic technique based on creative social systems suported on music composition concepts. This technique, called “Musical Composition Method” (MMC), which was proposed in Mora-Gutiérrez et al. (Artif Intell Rev 2012) as well as a variant, are presented in this study. The performance of MMC is evaluated and analyzed over forty instances drawn from twenty-two benchmark global optimization problems. The solutions obtained by the MMC algorithm were compared with those of various versions of particle swarm optimizer and harmony search on the same problem set. The experimental results demonstrate that MMC significantly improves the global performances of the other tested metaheuristics on this set of multimodal functions.


Global optimization Metaheuristics Social algorithms  Socio-cultural system of creativity Musical composition 

Mathematics Subject Classification



  1. 1.
    Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Global Optim 31:635–672MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Berg S (2007) Alfred’s essentials of Jazz theory: a complete self-study course for all musicians. Alfred PublishingGoogle Scholar
  3. 3.
    Bersini H, Dorigo M, Langerman S, Seront G, Gambardella LM (1996) Results of the first international contest on evolutionary optimisation (1st iceo). In: International conference on evolutionary computation, pp 611–615.
  4. 4.
    Biles JA (1994) Genjam: a genetic algorithm for generating jazz solos. In: International computer music conference. Aarhus, Denmark. International Computer Music Association, pp 131–137Google Scholar
  5. 5.
    Birattari M (2009) Tuning metaheuristics: a machine learning perspective. Springer, BerlinzbMATHCrossRefGoogle Scholar
  6. 6.
    de Bono E (1993) El pensamiento práctico. Editorial Paidos, BuairesGoogle Scholar
  7. 7.
    Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10:646–657CrossRefGoogle Scholar
  8. 8.
    Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73CrossRefGoogle Scholar
  9. 9.
    Chelouaha R, Siarry P (2000) Tabu search applied to global optimization. Eur J Oper Res 23:256–270CrossRefGoogle Scholar
  10. 10.
    de los Cobos Silva SG, Close JG, Andrade MAG, Licona AEM (2010) Búsqueda y exploración estocástica. Universidad Autónoma Metropolitana, MexicoGoogle Scholar
  11. 11.
    Cope D (2000) The algorithmic composer. A-R Editions Inc, WisconsinGoogle Scholar
  12. 12.
    Cope D (2005) Computer model of musical creativity. MIT Press, LondonGoogle Scholar
  13. 13.
    Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet 26:29–41CrossRefGoogle Scholar
  14. 14.
    Dréo J, Pétrowski A, Siarry P, Taillard E (2006) Metaheuristics for hard optimization: methods and case studies. Springer, BerlinzbMATHGoogle Scholar
  15. 15.
    Fogel DB (1994) An introduction to simulated evolutionary optimization. IEEE Comput Intell Soc 5:3–14Google Scholar
  16. 16.
    Geem ZW (2009) Recent advances in harmony search algorithm. Springer, BerlinCrossRefGoogle Scholar
  17. 17.
    Geem ZW (2010) Music-inspired harmony search algorithm. Springer, New YorkCrossRefGoogle Scholar
  18. 18.
    Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  19. 19.
    Gessler N (2010) Fostering creative emergences in artificial cultures. In: Artificial life XII—Proceedings of the twelfth international conference on the synthesis and simulation of living systems, pp 669–676. MIT Press, New YorkGoogle Scholar
  20. 20.
    Heller K, Mönks F, Csikszentmihalyi M, Wolfe R (2000) The international handbook of giftedness and talent. Elsevier, New YorkGoogle Scholar
  21. 21.
    Horner A, Goldberg DE (1991) Genetic algorithms and computer assisted music composition. In: ICMC’91 proceedings music composition. International Computer Music Association, San Francisco, pp 479–482Google Scholar
  22. 22.
    Horst R, Hoang T (1996) Global optimization: deterministic approaches. Springer, BerlinzbMATHGoogle Scholar
  23. 23.
    Jacob B (1995) Composing with genetic algorithms. International Computer Music Association, pp 452–455Google Scholar
  24. 24.
    Jacob BL (1996) Algorithmic composition as a model of creativity. Organised Sound 1:157–165CrossRefGoogle Scholar
  25. 25.
    Joshi MC, Moudgalya KM (2004) Optimization: theory and practice. Alpha Science International LtdGoogle Scholar
  26. 26.
    Kenedy J, Eberhart RC (1995) Particle swarm optimization. International Conference Neuronal Networks, pp 1942–1948Google Scholar
  27. 27.
    Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proc IEEE Congr Evol Comput, pp 1671–1676Google Scholar
  28. 28.
    Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798CrossRefGoogle Scholar
  29. 29.
    Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933zbMATHCrossRefGoogle Scholar
  30. 30.
    Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295CrossRefGoogle Scholar
  31. 31.
    Liu YT (2000) Creativity or novelty? Cognitive-computational versus social-cultural. Des Stud 23: 261–276CrossRefGoogle Scholar
  32. 32.
    Luenberger DG (1984) Linear and nonlinear programming. Addison-Wesley, New YorkzbMATHGoogle Scholar
  33. 33.
    Mahdavia M, Fesangharyb M, Damangirb E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 1537–1579Google Scholar
  34. 34.
    Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 204–210Google Scholar
  35. 35.
    Molga M, Smutnicki C (2005) Test functions for optimization needs.
  36. 36.
    Mora-Gutiérrez R, Ramírez-Rodríguez J, Rincón-García E (2012) An optimization algorithm inspired by musical composition. Artif Intell Rev. doi: 10.1007/s10462-011-9309-8
  37. 37.
    Omran M, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198:643–656Google Scholar
  38. 38.
    Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216:830–848MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Parsopoulos KE, Vrahatis MN (2004) UPSO: a unified particle swarm optimization scheme. In: Lecture series on computational sciences, pp 868–873Google Scholar
  40. 40.
    Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of swarm intelligence symposium, pp 174–181Google Scholar
  41. 41.
    Pohlheim H (2006) Geatbx: genetic and evolutionary algorithm toolbox for use with matlab.
  42. 42.
    Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on simulation of social behavior. IEEE Trans Evol Comput 7:386–396CrossRefGoogle Scholar
  43. 43.
    Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the 3rd annual conference on evolutionary programming. World Scientific, Singapore, pp 131–139Google Scholar
  44. 44.
    Riley MJW, Jenkins KW, Thompson CP (2010) A study of early stopping, ensembling, and patchworking for cascade correlation neural networks. IAENG Int J Appl Math 40(4):307–316Google Scholar
  45. 45.
    Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions: a survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39: 263–278CrossRefGoogle Scholar
  46. 46.
    Shenton A (2008) Olivier Messiaen’s system of signs: notes towards understanding his music. AshgateGoogle Scholar
  47. 47.
    Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE Congr Evol Comput, pp 69–73Google Scholar
  48. 48.
    van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8:225–239CrossRefGoogle Scholar
  49. 49.
    Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Exp Syst Appl 37:2826–2837CrossRefGoogle Scholar
  50. 50.
    Weise T (2009) Global optimization algorithms and theory and application.
  51. 51.
    Yang XS (2010) Test problems in optimization. Engineering optimization: an introduction with metaheuristic applications. Wiley, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Roman Anselmo Mora-Gutiérrez
    • 1
  • Javier Ramírez-Rodríguez
    • 2
    • 3
  • Eric Alfredo Rincón-García
    • 2
  • Antonin Ponsich
    • 2
  • Oscar Herrera
    • 2
  1. 1.Posgrado de IngenieríaUniversidad Nacional Autónoma de MéxicoMexicoMexico
  2. 2.Departamento de SistemasUniversidad Autónoma MetropolitanaMexicoMexico
  3. 3.LIA Université d’Avignon et des Pays de VaucluseAvignon CedexFrance

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