Constrained global optimization: algorithms and applications
Constrained global optimization: algorithms and applications
Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
A filled function method for finding a global minimizer of a function of several variables
Mathematical Programming: Series A and B
Zero duality gap for a class of nonconvex optimization problems
Journal of Optimization Theory and Applications
Convexification of a noninferior frontier
Journal of Optimization Theory and Applications
Local convexification of the Lagrangian function in nonconvex optimization
Journal of Optimization Theory and Applications
A New Filled Function Method for Global Optimization
Journal of Global Optimization
Journal of Global Optimization
Convexification and Concavification for a General Class of Global Optimization Problems
Journal of Global Optimization
Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method
Journal of Heuristics
Global optimization of signomial mixed-integer nonlinear programming problems with free variables
Journal of Global Optimization
Global descent methods for unconstrained global optimization
Journal of Global Optimization
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A convexification method is proposed for solving a class of global optimization problems with certain monotone properties. It is shown that this class of problems can be transformed into equivalent concave minimization problems using the proposed convexification schemes. An outer approximation method can then be used to find the global solution of the transformed problem. Applications to mixed-integer nonlinear programming problems arising in reliability optimization of complex systems are discussed and satisfactory numerical results are presented.