An improved neighbor cooperation using adjacent node cooperation in peer to peer networks

Abstract

The peer-to-peer (P2P) computation is that resource of the computer where sharing takes place through direct exchange. A Computational optimization is now getting much more prevalent in various fields in which there are simple solutions to a problem that are analytical. The swarm intelligence is defined as the behaviour of the artificial, natural, self-organized and decentralized systems. The multi-objective optimization (MOO) also known as the vector optimization is optimized in place of a single objective. The artificial fish swarm optimization (AFSA) has a search capacity that is global and also has a strong robustness being insensitive to the initial values. In this study the MOO has been hybridized by a simulated annealing using a k-means clustering and the AFSA by means of using the cooperation of neighbours. This multi-objective system helps in easing the difficulties of being sensitive to the initial solutions. This paper introduces an AFSA multi-objective system that promises to improve the neighbour cooperation in the application of P2P network.