Global Optimization of Nonconvex MINLP’s by Interval Analysis
In this work, we introduce a global optimization algorithm based on interval analysis for solving nonconvex Mixed Integer Nonlinear Programs (MINLPs). The algorithm is a generalization of the procedure proposed by the authors (Vaidyanathan and ElHalwagi, 1994a) for solving nonconvex Nonlinear Programs (NLPs) globally. The algorithm features several tools for accelerating the convergence to the global solution. A new discretization procedure is proposed within the framework of interval analysis for partitioning the search space. Furthermore, infeasible search spaces are eliminated without directly checking the constraints. Illustrative examples are solved to demonstrate the applicability of the proposed algorithm to solve nonconvex MINLPs efficiently.
KeywordsObjective Function Search Space Global Optimization Global Solution Interval Analysis
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