Algorithm 829

ACM Transactions on Mathematical Software - Tập 29 Số 4 - Trang 469-480 - 2003
Marco Gaviano1, Dmitri E. Kvasov2, Daniela Lera1, Yaroslav D. Sergeyev3
1Università di Cagliari, Cagliari, Italy
2Università di Roma "La Sapienza" and University of Nizhni Novgorod, Italy
3Università della Calabria and University of Nizhni Novgorod, Italy

Tóm tắt

A procedure for generating non-differentiable, continuously differentiable, and twice continuously differentiable classes of test functions for multiextremal multidimensional box-constrained global optimization is presented. Each test class consists of 100 functions. Test functions are generated by defining a convex quadratic function systematically distorted by polynomials in order to introduce local minima. To determine a class, the user defines the following parameters: (i) problem dimension, (ii) number of local minima, (iii) value of the global minimum, (iv) radius of the attraction region of the global minimizer, (v) distance from the global minimizer to the vertex of the quadratic function. Then, all other necessary parameters are generated randomly for all 100 functions of the class. Full information about each test function including locations and values of all local minima is supplied to the user. Partial derivatives are also generated where possible.

Từ khóa


Tài liệu tham khảo

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ACM Transactions on Mathematical Software

Tập 29 Số 4

469-480

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