A Particle Swarm Optimization Algorithm for Neighbor Selection in Peer-to-Peer Networks

Sun, Shichang; Abraham, Ajith; Zhang, Guiyong; Liu, Hongbo

doi:10.1109/cisim.2007.6

Cited by 11 publications

(6 citation statements)

References 14 publications

(13 reference statements)

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“…Return Q best function optimization problems, or the problems [33] that can be transformed to function optimization problems. As an algorithm, its main strength is its fast convergence [34,35], which compares favorably with many other global optimization algorithms. The classical particle swarm model consists of a swarm of particles which are initialized with a population of random candidate solutions.…”

Section: Model Selection Algorithmmentioning

confidence: 99%

Two-Stage Model Selection With Parameters Weighted Hidden Markov Models and Likelihood Ratio for Part-of-Speech Tagging

Sun¹,

Liu

Zhao

et al. 2012

NNW

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Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

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Section: Model Selection Algorithmmentioning

confidence: 99%

Two-Stage Model Selection With Parameters Weighted Hidden Markov Models and Likelihood Ratio for Part-of-Speech Tagging

Sun¹,

Liu

Zhao

et al. 2012

NNW

Self Cite

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show abstract

“…In order to maximize the disjointness of content, the system has to maximize the number of content pieces each peer can contribute to its neighbors by determining the connections. We discussed to apply the particle swarm algorithm for the problem (35,1400,17) [26]. The algorithm was repeated 4 times with different random seeds.…”

Section: Curve Illustrationmentioning

confidence: 99%

“…Each trial had a fixed number of 80 iterations. Other specific parameter settings of the algorithms are described in [26]. The average fitness values of the best solutions throughout the optimization run were recorded.…”

Section: Curve Illustrationmentioning

confidence: 99%

Convergence analysis of swarm algorithm

Liu

Abraham

Snášel

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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“…Huang et al in [20] propose a network-aware P2P file sharing architecture, which has a resource provider selection algorithm that can select a new resource provider for mobile peers experiencing broken connections in wireless mobile networks, but this resource selection strategy cannot be used in the P2P streaming system. Sun et al in [21] propose an optimization method using particle swarm optimization algorithm for neighbor selection in P2P networks. Yao et al in [22] indicate that PPLive achieves high ISP level traffic locality spontaneously with its decentralized, latency based, and neighbor referral peer selection strategy, which provide some new insights for better understanding and optimizing the network-and user-level performance in practical P2P live streaming systems.…”

Section: Related Workmentioning

confidence: 99%

Peer Selection Strategy Using Mobile Agent and Trust in Peer-to-Peer Streaming Media System

Huang

Wang

et al. 2013

International Journal of Distributed Sensor Networks

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The aim of this study is to improve the quality of service (QoS) for peer-to-peer (P2P) streaming media system with the proposed peer selection strategy. P2P technology is promising for large scale streaming media files distribution over the Internet. As peers contribute their resources over multipeer transmission paths, it is particularly challenging to achieve consistently high streaming QoS such as low start-up delay, low jitter, low packet loss ratio, and high bandwidth assurance. Recently, there are a lot of researchers studying peer selection strategy in peer-to-peer streaming system. In this paper, a novel P2P streaming media peer selection strategy based on mobile agent and trust (MATPS) is proposed. The proposed approach is compared with bandwidth first peer selection strategy (BFPSS), GridMedia's peer selection (GridPS), trust-based peer selection strategy (TrBPS), and SmartPeerCast peer selection strategy (SPCPS), and the simulation results show that the proposed strategy improves the streaming QoS.

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A Particle Swarm Optimization Algorithm for Neighbor Selection in Peer-to-Peer Networks

Abstract: Abstract

Cited by 11 publications

References 14 publications

Two-Stage Model Selection With Parameters Weighted Hidden Markov Models and Likelihood Ratio for Part-of-Speech Tagging

Two-Stage Model Selection With Parameters Weighted Hidden Markov Models and Likelihood Ratio for Part-of-Speech Tagging

Convergence analysis of swarm algorithm

Peer Selection Strategy Using Mobile Agent and Trust in Peer-to-Peer Streaming Media System

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