This paper addresses the development of efficient global search methods for fractional programming problems. Such problems are, in general, nonconvex (with numerous local extremums) and belong to a class of global opt...
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This paper addresses the development of efficient global search methods for fractional programming problems. Such problems are, in general, nonconvex (with numerous local extremums) and belong to a class of global optimization problems. First, we reduce a rather general fractional programming problem with d.c. functions to solving an equation with a vectorparameter that satisfies some nonnegativity assumption. This theorem allows the justified use of the generalized Dinkelbach's approach for solving fractional programming problems with a d.c. goal function. Based on solving of some d.c. minimization problem, we developed a global search algorithm for fractional programming problems, which was tested on a set of low-dimensional test problems taken from the literature as well as on randomly generated problems with up to 200 variables or 200 terms in the sum. (C) 2017 Elsevier Inc. All rights reserved.
This paper addresses a rather general fractional optimization problem. There are two ways to reduce the original problem. The first one is a solution of an equation with the optimal value of an auxiliary d.c. optimiza...
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ISBN:
(纸本)9783319694047;9783319694030
This paper addresses a rather general fractional optimization problem. There are two ways to reduce the original problem. The first one is a solution of an equation with the optimal value of an auxiliary d.c. optimization problem with a vectorparameter. The second one is to solve the second auxiliary problem with nonlinear inequality constraints. Both auxiliary problems turn out to be d.c. optimization problems, which allows to apply Global Optimization Theory [1 1 ,12] and develop two corresponding global search algorithms that have been tested on a number of test problems from the recent publications.
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