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

ACM Transactions on Mathematical Software - Tập 13 Số 3 - Trang 262-280 - 1987
Angelo Corana1, Michele Marchesi1, Claudio Martini1, Sandro Ridella1
1Istituto per i Circuiti Elettronici-C.N.R., Genoa, Italy

Tóm tắt

A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization. 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. 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. 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.

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Tài liệu tham khảo

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