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Relevant bibliographies by topics / Greedy randomized algorithm

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Academic literature on the topic 'Greedy randomized algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Greedy randomized algorithm"

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Almutairi, Abdulwahab. "Combined Nearest Greedy Algorithm With Randomized Iterated Greedy Algorithm to Solve Waste Collection Problem." International Journal of Statistics and Probability 9, no. 3 (2020): 66. http://dx.doi.org/10.5539/ijsp.v9n3p66.

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The waste collection considers as one of the common transportation problems in the operation research and management area and also it is a significant activity in each city. In this kind of the paper, we study a version of a real-life waste collection and try to find an efficient way to reduce the costs such as the cost of the operation such as fuel and maintenances, the cost of the environment such as noise and traffic congestions, the cost of the investment such as vehicles fleet. The waste collection problem can be formulated as a well-known Vehicle Routing Problem (VRP). The basic idea is

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Hu, Ta-Yin, and Li-Wen Chen. "Traffic Signal Optimization with Greedy Randomized Tabu Search Algorithm." Journal of Transportation Engineering 138, no. 8 (2012): 1040–50. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000404.

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Ortiz, Carlos, Adriana Lara, Jesús González, and Ayse Borat. "A Randomized Greedy Algorithm for Piecewise Linear Motion Planning." Mathematics 9, no. 19 (2021): 2358. http://dx.doi.org/10.3390/math9192358.

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We describe and implement a randomized algorithm that inputs a polyhedron, thought of as the space of states of some automated guided vehicle R, and outputs an explicit system of piecewise linear motion planners for R. The algorithm is designed in such a way that the cardinality of the output is probabilistically close (with parameters chosen by the user) to the minimal possible cardinality.This yields the first automated solution for robust-to-noise robot motion planning in terms of simplicial complexity (SC) techniques, a discretization of Farber’s topological complexity TC. Besides its rele

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GAMARNIK, DAVID, and DAVID A. GOLDBERG. "Randomized Greedy Algorithms for Independent Sets and Matchings in Regular Graphs: Exact Results and Finite Girth Corrections." Combinatorics, Probability and Computing 19, no. 1 (2009): 61–85. http://dx.doi.org/10.1017/s0963548309990186.

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We derive new results for the performance of a simple greedy algorithm for finding large independent sets and matchings in constant-degree regular graphs. We show that forr-regular graphs withnnodes and girth at leastg, the algorithm finds an independent set of expected cardinalitywheref(r) is a function which we explicitly compute. A similar result is established for matchings. Our results imply improved bounds for the size of the largest independent set in these graphs, and provide the first results of this type for matchings. As an implication we show that the greedy algorithm returns a nea

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Skorin-Kapov, Nina, Ricardo Santos, Hakim Ghazzai, and Andreas Kassler. "A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration." Electronics 10, no. 4 (2021): 434. http://dx.doi.org/10.3390/electronics10040434.

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In this paper, we consider the reconfiguration of wireless backhaul networks with mechanically steerable antennas in the presence of changing traffic demands. Reconfiguration requires the scheduling and coordination of several operations, including antenna alignment and link establishment/removal, with minimal disruption to existing user traffic. Previously, we proposed a Mixed Integer Linear Program (MILP) to orchestrate such reconfiguration with minimal packet loss. While the MILP solves the problem optimally for a limited number of discrete reconfiguration time slots, it does not scale well

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Kazakovtsev, Lev, Dmitry Stashkov, Mikhail Gudyma, and Vladimir Kazakovtsev. "Algorithms with greedy heuristic procedures for mixture probability distribution separation." Yugoslav Journal of Operations Research 29, no. 1 (2019): 51–67. http://dx.doi.org/10.2298/yjor171107030k.

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For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighbourhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighbourhoods to search

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Zhang, Yongfei, Jun Wu, Liming Zhang, Peng Zhao, Junping Zhou, and Minghao Yin. "An Efficient Heuristic Algorithm for Solving Connected Vertex Cover Problem." Mathematical Problems in Engineering 2018 (September 6, 2018): 1–10. http://dx.doi.org/10.1155/2018/3935804.

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The connected vertex cover (CVC) problem, which has many important applications, is a variant of the vertex cover problem, such as wireless network design, routing, and wavelength assignment problem. A good algorithm for the problem can help us improve engineering efficiency, cost savings, and resources consumption in industrial applications. In this work, we present an efficient algorithm GRASP-CVC (Greedy Randomized Adaptive Search Procedure for Connected Vertex Cover) for CVC in general graphs. The algorithm has two main phases, i.e., construction phase and local search phase. In the constr

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Yang, Chun-de, Ling Zhao, Kun-xian Shu, and Ling Wang. "Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure." Journal of Mathematics and Informatics 12 (February 28, 2018): 63–77. http://dx.doi.org/10.22457/jmi.v12a7.

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Verbeek, J. J., N. Vlassis, and B. Kröse. "Efficient Greedy Learning of Gaussian Mixture Models." Neural Computation 15, no. 2 (2003): 469–85. http://dx.doi.org/10.1162/089976603762553004.

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This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points an

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Wang, Shouqiang, and Sheng Zhang. "Metric k-median Problem and Its Application in Reverse Greedy Randomized Algorithm." Open Automation and Control Systems Journal 6, no. 1 (2015): 410–13. http://dx.doi.org/10.2174/1874444301406010410.

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Dissertations / Theses on the topic "Greedy randomized algorithm"

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Shahdeo, Sandhya. "MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298042586.

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Book chapters on the topic "Greedy randomized algorithm"

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Benedettini, Stefano, Christian Blum, and Andrea Roli. "A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction Problem." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13800-3_4.

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Brunato, Mauro, and Roberto Battiti. "A Multistart Randomized Greedy Algorithm for Traffic Grooming on Mesh Logical Topologies." In Next Generation Optical Network Design and Modelling. Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35670-9_27.

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Geyer-Schulz, Andreas, and Michael Ovelgönne. "The Randomized Greedy Modularity Clustering Algorithm and the Core Groups Graph Clustering Scheme." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01264-3_2.

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Marinakis, Yannis, Magdalene Marinaki, and Nikolaos Matsatsinis. "A Hybrid Clustering Algorithm Based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92695-5_11.

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Battiti, R., A. Bertossi, and R. Rizzi. "Randomized greedy algorithms for the hypergraph partitioning problem." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, 1998. http://dx.doi.org/10.1090/dimacs/043/02.

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Fischer, Manuela, and Andreas Noever. "Tight Analysis of Parallel Randomized Greedy MIS." In Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975031.140.

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Kliemann, Lasse, and Anand Srivastav. "Experimental Study of Non-oblivious Greedy and Randomized Rounding Algorithms for Hypergraph b-Matching." In Experimental Algorithms. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02011-7_18.

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Silva, Geiza C., Luiz S. Ochi, and Simone L. Martins. "Experimental Comparison of Greedy Randomized Adaptive Search Procedures for the Maximum Diversity Problem." In Experimental and Efficient Algorithms. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24838-5_37.

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Cruz-Reyes, Laura, Arturo Lam Alvarez, Marcela Quiroz-Castellanos, et al. "A Hybrid Metaheuristic Algorithm for the Quay Crane Scheduling Problem." In Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9779-9.ch012.

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This chapter presents a novel hybrid algorithm for the quay crane scheduling problem (QCSP). QCSP consists of scheduling a sequence of unloading and loading movements for cranes assigned to a vessel, minimizing the total completion time of all the tasks. The proposed algorithm integrates two well-known metaheuristics: Greedy Randomized Adaptive Search Procedure and Ant Colony System; it also incorporates a repositioning strategy of idle cranes to reduce the interference generated by the quay cranes. The experimental results show that the proposed method is able to find quality solutions in short times. In experiments reported in the literature with crafted instances of QCSP, heuristic running time varies from seconds in small instances to hours in instances of medium size. Currently, the industry requirements are up to a maximum of approximately five minutes. The hybrid algorithm presented in this chapter allows addressing these requirements, by finding good quality solutions in significantly shorter time for larger problems, which represents an advantage in real environments.

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Bouzidi, Dalel. "A Variable Neighborhood Search Algorithm to Solve the Flow Shop-Scheduling Problem Through Blocking." In Transportation, Logistics, and Supply Chain Management in Home Healthcare. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0268-6.ch004.

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In this chapter, we deal with the flow shop-scheduling problem through blocking known to be difficult, where there is no space and a task remains blocked on a machine until the next machine is available. For this reason, we propose the heuristic approach to minimize the delay (tardiness) as an optimization criterion. This chapter proposes a VNS-based heuristic the solutions of which are compared to those of the metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). We have developed a heuristic-based VNS to get better solutions in a reasonable time. Finally, comparisons with optimal solutions for small problems have shown that all versions can give good results; it would be interesting to extend the ideas presented in this document to the blocking flow shop at minimizing the total delay.

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Conference papers on the topic "Greedy randomized algorithm"

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Shouqiang, Wang. "Research on randomized greedy algorithm for k-median problem." In 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE). IEEE, 2011. http://dx.doi.org/10.1109/urke.2011.6007856.

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Mukherjee, Maharaj, and Kanad Chakraborty. "A Randomized Greedy Algorithm for the Pattern Fill Problem for DFM Applications." In 2008 9th International Symposium of Quality of Electronic Design (ISQED). IEEE, 2008. http://dx.doi.org/10.1109/isqed.2008.4479753.

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Stolyar, Alexander L., and Yuan Zhong. "A greedy randomized algorithm achieving sublinear optimality gap in a dynamic packing model." In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852247.

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Bouamama, Salim, and Christian Blum. "A randomized population-based iterated greedy algorithm for the minimum weight dominating set problem." In 2015 6th International Conference on Information and Communication Systems (ICICS). IEEE, 2015. http://dx.doi.org/10.1109/iacs.2015.7103193.

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Hashemi, Abolfazl, Mahsa Ghasemi, Haris Vikalo, and Ufuk Topcu. "A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8431563.

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Qian, Chao, Jing-Cheng Shi, Yang Yu, and Ke Tang. "On Subset Selection with General Cost Constraints." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/364.

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This paper considers the subset selection problem with a monotone objective function and a monotone cost constraint, which relaxes the submodular property of previous studies. We first show that the approximation ratio of the generalized greedy algorithm is $\frac{\alpha}{2}(1 \textendash \frac{1}{e^{\alpha}})$ (where $\alpha$ is the submodularity ratio); and then propose POMC, an anytime randomized iterative approach that can utilize more time to find better solutions than the generalized greedy algorithm. We show that POMC can obtain the same general approximation guarantee as the generalize

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Stolyar, Alexander L., and Yuan Zhong. "An infinite server system with general packing constraints: Asymptotic optimality of a greedy randomized algorithm." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736576.

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Cohen, Eldan, Richard Valenzano, and Sheila McIlraith. "Type-WA*: Using Exploration in Bounded Suboptimal Planning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/557.

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Previous work on satisficing planning using greedy best-first search (GBFS) has shown that non-greedy, randomized exploration can help escape uninformative heuristic regions and solve hard problems faster. Despite their success when used with GBFS, such exploration techniques cannot be directly applied to bounded suboptimal algorithms like Weighted A* (WA*) without losing the solution-quality guarantees. In this work, we present Type-WA*, a novel bounded suboptimal planning algorithm that augments WA* with type-based exploration while still satisfying WA*'s theoretical solution-quality guarant

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Akrout, Hanen, Bassem Jarboui, Abdelwaheb Rebai, and Patrick Siarry. "New Greedy Randomized Adaptive Search Procedure based on differential evolution algorithm for solving no-wait flowshop scheduling problem." In 2013 International Conference on Advanced Logistics and Transport (ICALT). IEEE, 2013. http://dx.doi.org/10.1109/icadlt.2013.6568480.

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Gliesch, Alex, and Marcus Ritt. "A Genetic Algorithm For Fair Land Allocation." In XXXII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/ctd.2019.6342.

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The goal ofagrarian reform projects is the redistribution offarmland from large latifundia to smaller, often family farmers. One ofthe main problems the Brazilian National Institute ofColonization and Agrarian Reform (INCRA) has to solve is to subdivide a large parcel ofland into smaller lots that are balan- ced with respect to certain attributes. This problem is difficult since it considers several constraints originating from legislation as well as ethical considerati- ons. Current solutions are computer-assisted, but manual, time-consuming and error-prone, leading to rectangular lots ofsimi

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