Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks

2018 ◽  
Author(s):  
Jianghai Hu ◽  
Yingying Xiao ◽  
Ji Liu
Keyword(s):  
Distributed Algorithms ◽  
Optimization Problems ◽  
Agent Networks

scholarly journals Fully Distributed Algorithms for Convex Optimization Problems

2010 ◽  
Vol 20 (6) ◽  
pp. 3260-3279 ◽  
Author(s):  
Damon Mosk-Aoyama ◽  
Tim Roughgarden ◽  
Devavrat Shah
Keyword(s):  
Convex Optimization ◽  
Distributed Algorithms ◽  
Optimization Problems ◽  
Convex Optimization Problems

scholarly journals Self-Stabilizing Global Optimization Algorithms for Large Network Graphs

2005 ◽  
Vol 1 (3-4) ◽  
pp. 329-344 ◽  
Author(s):  
Wayne Goddard ◽  
Stephen T. Hedetniemi ◽  
David P. Jacobs ◽  
Pradip K. Srimani
Keyword(s):  
Global Optimization ◽  
Distributed Algorithms ◽  
Optimization Problems ◽  
Dominating Set ◽  
Optimization Algorithms ◽  
Large Network ◽  
Shared Variable ◽  
Large Networks ◽  
Total Dominating Set ◽  
Graph Optimization

The paradigm of self-stabilization provides a mechanism to design efficient localized distributed algorithms that are proving to be essential for modern day large networks of sensors. We provide self-stabilizing algorithms (in the shared-variable ID-based model) for three graph optimization problems: a minimal total dominating set (where every node must be adjacent to a node in the set) and its generalizations, a maximal k-packing (a set of nodes where every pair of nodes are more than distance k apart), and a maximal strong matching (a collection of totally disjoint edges).

scholarly journals On Constructing t -Spanner in IoT under SINR

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiujuan Zhang ◽  
Yongcai Wang ◽  
Wenping Chen ◽  
Yuqing Zhu ◽  
Deying Li ◽  
...  
Keyword(s):  
Distributed Algorithms ◽  
Optimization Problems ◽  
Time Slot ◽  
Randomized Algorithm ◽  
Independent Set ◽  
Task Completion ◽  
Time Interval ◽  
Communication Graph ◽  
Effectiveness And Efficiency ◽  
The Internet Of Things

Following the recent advances in the Internet of Things (IoT), it is drawing lots of attention to design distributed algorithms for various network optimization problems under the SINR (Signal-to-Interference-and-Noise-Ratio) interference model, such as spanner construction. Since a spanner can maintain a linear number of links while still preserving efficient routes for any pair of nodes in wireless networks, it is important to design distributed algorithms for spanners. Given a constant t > 1 as the required stretch factor, the problem of our concern is to design an efficient distributed algorithm to construct a t -spanner of the communication graph under SINR such that the delay for the task completion is minimized, where the delay is the time interval between the time slot that the first node commences its operation to the time slot that all the nodes finish their task of constructing the t -spanner. Our main contributions include four aspects. First, we propose a proximity range and proximity independent set (PISet) to increase the number of nodes transmitting successfully at the same time in order to reduce the delay. Second, we develop a distributed randomized algorithm SINR-Spanner to construct a required t -spanner with high probability. Third, the approximation ratio of SINR-Spanner is proven to be a constant. Finally, extensive simulations are carried out to verify the effectiveness and efficiency of our proposed algorithm.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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