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Discrete optimization

About: Discrete optimization is a research topic. Over the lifetime, 4598 publications have been published within this topic receiving 158297 citations. The topic is also known as: discrete optimisation.


Papers
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Book ChapterDOI
01 Jan 1997
TL;DR: In this paper, the generalized row sum method is shown to satisfy Self-Consistent Monotonicity and the results suggest that there are general limitations of the discrete optimization approach to preference aggregation.
Abstract: We consider methods for aggregating preferences based on discrete optimization. The preferences are represented by arbitrary binary relations (possibly weighted) or matrices of paired comparisons (possibly incomplete). The case of incomplete preferences remains practically unexplored so far. We examine properties of several known methods and propose one new method. Some results are established that characterize solutions of the related optimization problems. Necessary conditions of a new axiom called Self-Consistent Monotonicity are proved. The generalized row sum method is shown to satisfy Self-Consistent Monotonicity. The results suggest that there are general limitations of the discrete optimization approach to preference aggregation.

26 citations

Book
15 Sep 2011
TL;DR: The Boxstep method is used to maximize Lagrangean functions in the context of a branch-and-bound algorithm for the general discrete optimization problem.
Abstract: The Boxstep method is used to maximize Lagrangean functions in the context of a branch-and-bound algorithm for the general discrete optimization problem Results are presented for three applications: facility location, multi-item production scheduling, and single machine scheduling The performance of the Boxstep method is contrasted with that of the subgradient optimization method

26 citations

Journal ArticleDOI
TL;DR: In this paper, a non-standard angular contact ball bearing for the main shaft of a grinder was optimized using design automation and optimization techniques, and the results revealed that both stiffness values were enhanced while satisfying all design constraints.

26 citations

Proceedings ArticleDOI
07 Apr 1997
TL;DR: An extension of the method of Concurrent Subspace Optimization (CSSO) has been developed to accomodate mixed continuous/discrete design problems and demonstrates that the database of design information assembled during CSSO can be exploited to enhance the efficiency of subsequent runs.
Abstract: An extension of the method of Concurrent Subspace Optimization (CSSO) has been developed to accomodate mixed continuous/discrete design problems. The mixed CSSO framework employs artificial neural networks to provide approximations to the design space, which are the means of coordinating design decisions in the individual disciplines. This approach is applied to a nonhierarchic test problem which contains continuous and discrete design variables. The results demonstrate that the mixed CSSO framework is able to locate optimal designs and did reduce the number of the complete system analyses required by conventional optimization techniques. Computational resources remain a concern, however, due to the large number of contributing (disciplinary) analyses required to perform mixed optimization at the discipline level. Results demonstrate that the database of design information assembled during CSSO can be exploited to enhance the efficiency of subsequent runs, even if the requirements of the system design problem are altered.

26 citations

Proceedings ArticleDOI
15 Mar 2009
TL;DR: A novel discrete optimization approach is developed to optimally solve the optimization problem of power system shunt filter design based on Discrete Multi Objective Particle Swarm Optimization MOPSO technique to ensure harmonic current reduction and noise mitigation on electrical utility grid.
Abstract: In this paper, a novel discrete optimization approach is developed to optimally solve the optimization problem of power system shunt filter design based on Discrete Multi Objective Particle Swarm Optimization MOPSO technique to ensure harmonic current reduction and noise mitigation on electrical utility grid. In this novel optimization approach, Multi Objective Particle Swarm Optimization MOPSO is implemented to tackle a number of conflicting goals that define the optimality problem. This paper deals with three conflicting objective functions. These conflicting functions are: 1. Minimum harmonic current penetration into the electric grid system, 2. Maximum harmonic current absorption by the harmonic power filter, 3. Minimum harmonic voltage distortion at the point of common coupling, Throughout the optimization process, all power filter parameters are being treated as either continuous or discrete variables. The shunt power filter design and optimization is performed over a specified set of discrete dominant offending harmonics.

25 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202313
202236
2021104
2020128
2019113
2018140