Topic
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.
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TL;DR: A novel algorithm, dubbed as weighted BCO (wBCO), that allows the bees to search in the solution space deliberately while considering policies to share the attained information about the food sources heuristically, exhibiting the superiority of wBCO over the competitor approaches.
30 citations
TL;DR: The experimental results and comparisons show that the proposed Jaya algorithm, called DJAYA, is highly competitive and robust optimizer for the problem dealt with, which is one of the famous discrete problems in the discrete optimization.
30 citations
TL;DR: Strong and weak variations of PAS are defined in the setting of finite global optimization and analogous results are proved.
Abstract: Pure Adaptive Search is a stochastic algorithm which has been analyzed for continuous global optimization. When a uniform distribution is used in PAS, it has been shown to have complexity which is linear in dimension. We define strong and weak variations of PAS in the setting of finite global optimization and prove analogous results. In particular, for then-dimensional lattice {1,⋯,k}n, the expected number of iterations to find the global optimum is linear inn. Many discrete combinatorial optimization problems, although having intractably large domains, have quite small ranges. The strong version of PAS for all problems, and the weak version of PAS for a limited class of problems, has complexity the order of the size of the range.
30 citations
TL;DR: In this article, a branch and bound method for solving continuous global optimization problems can be adapted to the discrete case, and an algorithm for minimizing a concave function over the integers contained in a compact polyhedron is presented.
Abstract: In this note we show that various branch and bound methods for solving continuous global optimization problems can be readily adapted to the discrete case. As an illustration, we present an algorithm for minimizing a concave function over the integers contained in a compact polyhedron. Computational experience with this algorithm is reported.
30 citations
TL;DR: A simple method for achieving a discrete optimum design from a segmental optimum design is described, avoiding the combinatorial nature of discrete optimization by introducing the concept of segmental members.
Abstract: A simple method based upon linear programming is described for the design of minimum weight structures under the restrictions that member sizes and/or material properties may be chosen only from discrete sets. The types of structures considered are those composed of axial force bars, membrane plates and shear panels. The method avoids the combinatorial nature of discrete optimization by introducing the concept of segmental members. The segmental optimum design is found by linear programming. Its weight is a lower bound to the weight of the discrete optimum design. A simple method for achieving a discrete optimum design from a segmental optimum design is described. Several examples of discrete optimum truss designs are presented.
30 citations