Journal of Optimization Theory and Applications

, Volume 76, Issue 3, pp 501–521

Shuffled complex evolution approach for effective and efficient global minimization


  • Q. Y. Duan
    • Department of Hydrology and Water ResourcesThe University of Arizona
  • V. K. Gupta
    • Department of Hydrology and Water ResourcesThe University of Arizona
  • S. Sorooshian
    • Department of Hydrology and Water ResourcesThe University of Arizona
    • Department of Systems and Industrial EngineeringThe University of Arizona
Contributed Papers

DOI: 10.1007/BF00939380

Cite this article as:
Duan, Q.Y., Gupta, V.K. & Sorooshian, S. J Optim Theory Appl (1993) 76: 501. doi:10.1007/BF00939380


The degree of difficulty in solving a global optimization problem is in general dependent on the dimensionality of the problem and certain characteristics of the objective function. This paper discusses five of these characteristics and presents a strategy for function optimization called the shuffled complex evolution (SCE) method, which promises to be robust, effective, and efficient for a broad class of problems. The SCE method is based on a synthesis of four concepts that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches; (b) clustering; (c) systematic evolution of a complex of points spanning the space, in the direction of global improvement; and (d) competitive evolution. Two algorithms based on the SCE method are presented. These algorithms are tested by running 100 randomly initiated trials on eight test problems of differing difficulty. The performance of the two algorithms is compared to that of the controlled random search CRS2 method presented by Price (1983, 1987) and to a multistart algorithm based on the simplex method presented by Nelder and Mead (1965).

Key Words

Numerical optimizationglobal searchefficiencyeffectivenesscomplex of pointsrandom searchcompetitive evolution

Copyright information

© Plenum Publishing Corporation 1993