Algorithm 744: a stochastic algorithm for global optimization with constraints

Authors:
F. Michael Rabinowitz
Affiliations:
Memorial Univ. of Newfoundland, St. John's, Nfld., Canada
Venue:
ACM Transactions on Mathematical Software (TOMS)
Year:
1995

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Abstract

A stochastic algorithm is presented for finding the global optimum of a function of n variables subject to general constraints. The algorithm is intended for moderate values of n, but it can accommodate objective and constraint functions that are discontinuous and can take advantage of parallel processors. The performance of this algorithm is compared to that of the Nelder-Mead Simplex algorithm and a Simulated Annealing algorithm on a variety of nonlinear functions. In addition, one-, two-, four-, and eight-processor versions of the algorithm are compared using 64 of the nonlinear problems with constraints collected by Hock and Schittkowski. In general, the algorithm is more robust than the Simplex algorithm, but computationally more expensive. The algorithm appears to be as robust as the Simulated Annealing algorithm, but computationally cheaper. Issues discussed include algorithm speed and robustness, applicability to both computer and mathematical models, and parallel efficiency.