Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: In this paper, an effective heuristic algorithm to solve a scheduling problem that comes from industry is proposed, where the workshop is an hybrid flow shop with recirculation and the problem is to perform jobs between a release date and a due date, in order to minimize the weighted number of tardy jobs.
Abstract: We propose in this paper an effective heuristic algorithm to solve a scheduling problem that comes from industry. The workshop is an hybrid flow shop with recirculation and the problem is to perform jobs between a release date and a due date, in order to minimize the weighted number of tardy jobs. Firstly, an integer linear programming formulation of the problem is proposed, then a lower bound, a greedy algorithm and a genetic algorithm are described as approximate methods. To evaluate these heuristics, experiences on instances like industrial ones are computed, and show the efficiency of the genetic algorithm.
118 citations
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26 Jun 2004TL;DR: It is shown that randomized search heuristics find minimum spanning trees in expected polynomial time without employing the global technique of greedy algorithms.
Abstract: Randomized search heuristics, among them randomized local search and evolutionary algorithms, are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization The analysis of these randomized search heuristics has been started for some well-known problems, and this approach is followed here for the minimum spanning tree problem After motivating this line of research, it is shown that randomized search heuristics find minimum spanning trees in expected polynomial time without employing the global technique of greedy algorithms
118 citations
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04 Jun 2012TL;DR: New techniques for reducing the depth of circuits for cryptographic applications are described and the result, when applied to the AES S-Box, is a circuit with depth 16 and only 128 gates.
Abstract: New techniques for reducing the depth of circuits for cryptographic applications are described. These techniques also keep the number of gates quite small. The result, when applied to the AES S-Box, is a circuit with depth 16 and only 128 gates. For the inverse, it is also depth 16 and has only 127 gates. There is a shared middle part, common to both the S-Box and its inverse, consisting of 63 gates. The best previous comparable design for the AES S-Box has depth 22 and size 148 [12].
118 citations
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TL;DR: This study shows that GRG guarantees optimal or near optimal coverage radius, and is the first localized sensor self-deployment algorithms that provide such coverage guarantee.
Abstract: We consider sensor self-deployment problem, constructing FOCUSED coverage (F-coverage) around a Point of Interest (POI), with novel evaluation metric, coverage radius. We propose to deploy sensors in polygon layers over a locally computable equilateral triangle tessellation (TT) for optimal F-coverage formation, and introduce two types of deployment polygon, H-polygon and C-polygon. We propose two strictly localized solution algorithms, Greedy Advance (GA), and Greedy-Rotation-Greedy (GRG). The two algorithms drive sensors to move along the TT graph to surround POI. In GA, nodes greedily proceed as close to POI as they can; in GRG, when their greedy advance is blocked, nodes rotate around POI along locally computed H- or C-polygon to a vertex where greedy advance can resume. We prove that they both yield a connected network with maximized hole-free area coverage. To our knowledge, they are the first localized sensor self-deployment algorithms that provide such coverage guarantee. We further analyze their coverage radius property. Our study shows that GRG guarantees optimal or near optimal coverage radius. Through extensive simulation we as well evaluate their performance on convergence time, energy consumption, and node collision.
118 citations
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TL;DR: The dual of the formulation is shown to be a disjunctive graph model, which is well known from scheduling theory, and a longest path algorithm is used to obtain bounding information for subproblems in a branch and bound solution procedure.
Abstract: The time-constrained traveling salesman problem is a variation of the familiar traveling salesman problem that includes time window constraints on the time a particular city, or cities, may be visited. This note presents a concise formulation of the time-constrained traveling salesman problem. The model assumes that the distances of the problem are symmetrical and that the triangle inequality holds. Additionally, the model allows the salesman to wait at a city, if necessary, for a time window to open. The dual of the formulation is shown to be a disjunctive graph model, which is well known from scheduling theory. A longest path algorithm is used to obtain bounding information for subproblems in a branch and bound solution procedure. Computational results are presented for several small to moderate size problems.
118 citations