Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here

DOI Web Site 24 Citations
  • A. Corana
    Istituto per i Circuiti Elettronici-C.N.R., Genoa, Italy
  • M. Marchesi
    Istituto per i Circuiti Elettronici-C.N.R., Genoa, Italy
  • C. Martini
    Istituto per i Circuiti Elettronici-C.N.R., Genoa, Italy
  • S. Ridella
    Istituto per i Circuiti Elettronici-C.N.R., Genoa, Italy

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<jats:p>A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization.</jats:p> <jats:p>The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered.</jats:p> <jats:p>The algorithm has been tested against the Nelder and Mead simplex method and against a version of Adaptive Random Search. The test functions were Rosenbrock valleys and multiminima functions in 2,4, and 10 dimensions.</jats:p> <jats:p>The new method proved to be more reliable than the others, being always able to find the optimum, or at least a point very close to it. It is quite costly in term of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.</jats:p>

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