Summary
Peer-to-peer (P2P) topology has significant influence on the performance, search efficiency and functionality, and scalability of the application. In this Chapter, we introduce the problem of neighbor selection in peer-to-peer networks using two population based meta-heuristics: Particle Swarm Optimization (PSO) algorithms and Genetic Algorithms (GAs). Both a single objective and a multi-objective problem are formulated, and then the P2P neighbor selection problem is defined. We present the neighbor selection strategy based on PSO and GA algorithm. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. We also discuss the characteristics of ergodicity during particle swarm searching process. We also illustrate the algorithm performance and trace its feasibility and effectiveness with the help of some examples.
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Liu, H., Abraham, A., Xhafa, F. (2008). Peer-to-Peer Neighbor Selection Using Single and Multi-objective Population-Based Meta-heuristics. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69277-5_12
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DOI: https://doi.org/10.1007/978-3-540-69277-5_12
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