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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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Journal ArticleDOI
TL;DR: A complete coverage path planning model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost.

97 citations

Proceedings ArticleDOI
06 Jul 2020
TL;DR: This work designs a novel two-stage latency-aware VNF deployment scheme, highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning V NFs by reusing as many VNFs as possible.
Abstract: With the increasing demand of low-latency network services, mobile edge computing (MEC) emerges as a new paradigm, which provides server resources and processing capacities in close proximity to end users. Based on network function virtualization (NFV), network services can be flexibly provisioned as virtual network function (VNF) chains deployed at edge servers. However, due to the resource shortage at the network edge, how to efficiently deploy VNF chains with latency guarantees and resource efficiency remains as a challenging problem. In this work, we focus on jointly optimizing the resource utilization of both edge servers and physical links under the latency limitations. Specifically, we formulate the VNF chain deployment problem as a mixed integer linear programming (MILP) to minimize the total resource consumption. We design a novel two-stage latency-aware VNF deployment scheme: highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning VNFs by reusing as many VNFs as possible. We demonstrate that our proposed algorithm can efficiently achieve a near-optimal solution with a theoretically-proved worstcase performance bound. Extensive simulation results show that the proposed algorithm outperforms three previous heuristic algorithms.

96 citations

Journal ArticleDOI
TL;DR: In this paper, a new heuristic algorithm is proposed to solve the one-dimensional bin-packing problem, which is optimal if the sum of requirements of items is less than or equal to twice the bin capacity.
Abstract: We describe a new heuristic algorithm to solve the one-dimensional bin-packing problem. The proposed algorithm is optimal if the sum of requirements of items is less than or equal to twice the bin capacity. Our computational results show that effectiveness of the proposed algorithm in finding optimal or near-optimal solutions is superior to that of the FFD and BFD algorithms, specifically for those so called 'difficult' problems that require an optimal solution to fill most of the bins, if not all, exactly to capacity.

96 citations

Journal ArticleDOI
TL;DR: It is shown that by adding a pheromone correction strategy and dedicating special attention to the initial condition of the ACO algorithm this negative effect can be avoided and it is possible to achieve good results without using the complex two-step ACO algorithms previously developed.
Abstract: In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easily trapped in local optima. We have shown that by adding a pheromone correction strategy and dedicating special attention to the initial condition of the ACO algorithm this negative effect can be avoided. Using this approach it is possible to achieve good results without using the complex two-step ACO algorithm previously developed. We have tested our method on standard benchmark data and shown that it is competitive to the existing algorithms.

96 citations

Proceedings ArticleDOI
01 May 1999
TL;DR: The results prove (if P#NP) that the viewselection problem is essentially inapproximable for general partial orders, and studies of the Harinarayan, Rajaraman and Ullman framework and its generalizations should focus on special cases of practical significance, such as hypercubes, and on experimental comparison of heuristics.
Abstract: A commonly used and powerful technique for improving query response time over very large databases is to precompute (‘Lmaterialize”) frequently’ asked queries (“views”). The problem is to select an appropriate set of views, given a limited amount of resources. Harinarayan, Rajaraman and Ullman formalized this technique by proposing a framework in which queries are modeled by a weighted partial order, and selecting a set of views whose materialization minimizes the average query response time is equivalent to selecting a subset of nodes of the partial order that minimizes a suitably defined cost function. Because this problem is NPHard, the focus is on approximability and heuristics. Harinarayan, Rajaraman and Ullman proposed a greedy heuristic together with a “benefit” criterion to measure its performance; this heuristic and performance measure are used in several subsequent papers which generalize their work. We prove the following lower bounds: (a) The greedy heuristic of Harinarayan, Rajaraman and Ullman has query response time at least n/12 times optimal for infinitely many n. (Compare this to the fact that no algorithm, regardless of how naive it is, ever has query response time exceeding n times optimal.) (b) If PfNP, then for every e > 0, every polynomialtime approximation algorithm for the view-selection problem will output solutions with query response time at least n’-’ times optimal, for infinitely many n, even for partial orders with bounded degrees and *College of Computing, Georgia Institute of Technology, howardQcc.gatech.edu. Research supported in part by NSF grant CCR-9732746. +CoIlege of Computing and School of Industrial and Systems Engineering, Georgia Institute of Technology, mihailQcc.gatech.edu. Work done in part while the author was at Bellcore. Permission to make digital or hard copies of all OT part of this work fbl personal or classroom USC is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this nhx and the full citation on the tirst page. TO COj>y otllerwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission andior a fee. PODS ‘99 Philadelphia PA Copyright ACM 1999 l-58113-062-7/99/05...$5.00 bounded depth. (c) A similar result applies even if we generously allow the algorithm to materialize ak views, LY a constant, and compare its performance to the optimal achievable when k views are chosen. Our results prove (if P#NP) that the viewselection problem is essentially inapproximable for general partial orders (the “benefit” performance measure of Harinarayan, Rajaraman and Ullman provides no competitiveness guarantee against the optimal solution). Hence studies of the Harinarayan, Rajaraman and Ullman framework and its generalizations should focus on special cases of practical significance, such as hypercubes, and on experimental comparison of heuristics.

96 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023350
2022690
2021809
2020939
20191,006
2018967