Soft Computing

, Volume 18, Issue 10, pp 1931–1948 | Cite as

Adaptation of the musical composition method for solving constrained optimization problems

  • Roman Anselmo Mora-Gutiérrez
  • Javier Ramírez-Rodríguez
  • Eric Alfredo Rincón-García
  • Antonin Ponsich
  • Oscar Herrera
  • Pedro Lara-Velázquez
Methodologies and Application


Many real-world problems may be expressed as nonlinear constrained optimization problems (CNOP). For this kind of problems, the set of constraints specifies the feasible solution space. In the last decades, several algorithms have been proposed and developed for tackling CNOP. In this paper, we present an extension of the “Musical Composition Method” (MMC) for solving constrained optimization problems. MMC was proposed by Mora et al. (Artif Intell Rev 1–15, doi: 10.1007/s10462-011-9309-8, 2012a). The MMC is based on a social creativity system used to compose music. We evaluated and analyzed the performance of MMC on 12 CNOP benchmark cases. The experimental results demonstrate that MMC significantly improves the global performances of the other tested metaheuristics on some benchmark functions.


Particle Swarm Optimization Decision Variable Differential Evolution Constrain Optimization Problem Constraint Violation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roman Anselmo Mora-Gutiérrez
    • 1
  • Javier Ramírez-Rodríguez
    • 1
    • 2
  • Eric Alfredo Rincón-García
    • 1
  • Antonin Ponsich
    • 1
  • Oscar Herrera
    • 1
  • Pedro Lara-Velázquez
    • 1
  1. 1.Departamento de SistemasUniversidad Autónoma Metropolitana D.F. MéxicoMéxico
  2. 2.LIA Université d’Avignon et des Pays de VaucluseAvignonFrance

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