A dynamic convexized method for nonconvex mixed integer nonlinear programming

We consider in this paper the nonconvex mixed-integer nonlinear programming problem. We present a mixed local search method to find a local minimizer of an unconstrained nonconvex mixed-integer nonlinear programming problem. Then an auxiliary function which has the same global minimizers and the sam...

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Veröffentlicht in:Computers & operations research Jg. 38; H. 12; S. 1792 - 1804
Hauptverfasser: Zhu, Wenxing, Lin, Geng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.12.2011
Elsevier
Pergamon Press Inc
Schlagworte:
Applied sciences
Auxiliary function
Benchmarking
Exact sciences and technology
Integer programming
Local search
Mathematical analysis
Mathematical models
Mathematical programming
Mixed integer
Nonconvex mixed-integer nonlinear programming
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Optimization algorithms
Parameter estimation
Search methods
Studies
Local search
Nonconvex mixed-integer nonlinear programming
Auxiliary function
Non convex programming
Minimization
Mixed integer programming
Non linear programming
Mixed method
Dynamic method
Penalty method
Search algorithm
ISSN:0305-0548, 1873-765X, 0305-0548
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Beschreibung
Zusammenfassung:We consider in this paper the nonconvex mixed-integer nonlinear programming problem. We present a mixed local search method to find a local minimizer of an unconstrained nonconvex mixed-integer nonlinear programming problem. Then an auxiliary function which has the same global minimizers and the same global minimal value as the original problem is constructed. Minimization of the auxiliary function using our local search method can escape successfully from previously converged local minimizers by taking increasing values of parameters. For the constrained nonconvex mixed-integer nonlinear programming problem, we develop a penalty based method to convert the problem into an unconstrained one, and then use the above method to solve the later problem. Numerical experiments and comparisons on a set of MINLP benchmark problems show the effectiveness of the proposed algorithm.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2011.02.014