Distributed Greedy Algorithm for Satellite Assignment Problem with Submodular Utility Function∗∗The work is supported by Lincoln Laboratory with award #7000292526.

In the online metric matching problem, there are servers on a given metric space and requests are given one-by-one. The task of an online algorithm is to match each request immediately and irrevocably with one of the unused servers. In this paper, we pursue competitive analysis for two variants of the online metric matching problem. The first variant is a restriction where each server is placed at one of two positions, which is denoted by OMM([Formula: see text]). We show that a simple greedy algorithm achieves the competitive ratio of 3 for OMM([Formula: see text]). We also show that this greedy algorithm is optimal by showing that the competitive ratio of any deterministic online algorithm for OMM([Formula: see text]) is at least 3. The second variant is the online facility assignment problem on a line. In this problem, the metric space is a line, the servers have capacities, and the distances between any two consecutive servers are the same. We denote this problem by OFAL([Formula: see text]), where [Formula: see text] is the number of servers. We first observe that the upper and lower bounds for OMM([Formula: see text]) also hold for OFAL([Formula: see text]), so the competitive ratio for OFAL([Formula: see text]) is exactly 3. We then show lower bounds on the competitive ratio [Formula: see text] [Formula: see text], [Formula: see text] [Formula: see text] and [Formula: see text] [Formula: see text] for OFAL([Formula: see text]), OFAL([Formula: see text]) and OFAL([Formula: see text]), respectively.

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A new greedy algorithm for the quadratic assignment problem

Optimization Letters ◽

10.1007/s11590-011-0409-9 ◽

2011 ◽

Vol 7 (2) ◽

pp. 207-220 ◽

Cited By ~ 2

Author(s):

Theodoros P. Gevezes ◽

Leonidas S. Pitsoulis

Keyword(s):

Greedy Algorithm ◽

Assignment Problem ◽

Quadratic Assignment Problem ◽

Quadratic Assignment

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Actuator Task Assignment Based on Auction Method in Wireless Sensor and Actuator Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.306 ◽

2013 ◽

Vol 373-375 ◽

pp. 306-310 ◽

Cited By ~ 3

Author(s):

Ping Deng ◽

Ke Li Zhang

Keyword(s):

Wireless Sensor Network ◽

Utility Function ◽

Sensor Network ◽

Assignment Problem ◽

Task Assignment ◽

Wireless Sensor ◽

Proper Action ◽

New Challenges ◽

Simulation Results ◽

Task Assignment Problem

A typical Wireless Sensor Network (WSN) only performs the action of sensing the environment, the needs of smart interaction with the environment have led to the emergence of Wireless Sensor and Actuator Network (WSAN). With the presence of actuators, WSAN is heterogeneous, which brings about new challenges that need to be addressed. In this paper, the task assignment of actuators in overlapping area is studied. Firstly, a new utility function is defined, which is the standard to choose the proper action actuators in overlapping area. Then, based on the new utility function, a distributed solution called localized auction method to solve task assignment problem in overlapping area is proposed. Simulation results demonstrate that the proposed auction method can assign tasks to the unique actuator and meet the action completion bound.

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Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽

10.33050/ccit.v12i2.687 ◽

2019 ◽

Vol 12 (2) ◽

pp. 170-176

Author(s):

Anggit Dwi Hartanto ◽

Aji Surya Mandala ◽

Dimas Rio P.L. ◽

Sidiq Aminudin ◽

Andika Yudirianto

Keyword(s):

Artificial Intelligence ◽

Greedy Algorithm ◽

Dijkstra Algorithm ◽

Testing Phase ◽

A Value ◽

Route Problem ◽

Starting Point ◽

The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

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