Over the recent years peer-to-peer (p2p) systems have become increasingly popular. As of today most of the internet IP traffic is already transmitted in this format and still it is said to double in volume till 2014. Most p2p systems, however, are not pure serverless solutions, nor is the searching in those networks highly efficient, usually achieved by simple flooding. In order to confront with the growing traffic we must consider more elaborate search mechanisms and far less centralized environments. An effective proposal to this problem is to solve it in the domain of Ant Colony Optimization metaheuristics. In this paper we present an overview of ACO algorithms that offer the best potential in this field, under the strict requirements and limitations of a pure p2p network. We design several experiments to serve as an evaluation platform for the mentioned algorithms to conclude the features of a high quality approach. Finally, we consider two hybrid extensions to the classical algorithms, in order to examine their contribution to the overall quality robustness.

The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee nontrivial efficiency. Some claim that ant colony optimization (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform well under the conditions of static networks, solving predetermined problems in a known and bound space. The question that remains to be answered is whether the evolutive component of these algorithms is able to cope with changing conditions; and by those we mean changes both in the positive sense, such as the appearance of new resources, but also in the negative sense, such as the disappearance or failure of fragments of the network. In this paper we study these considerations in depth, bearing in mind the specificity of the peer-to-peer nature.

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-The Ant Colony Optimization (ACO) has been a very resourceful metaheuristic over the past decade and it has been successfully used to approximately solve many static NPHard problems. There is a limit, however, of its applicability in the field of p2p networks; derived from the fact that such networks have the potential to evolve constantly and at a high pace, rendering the already-established results useless. In this paper we approach the problem by proposing a generic knowledge diffusion mechanism that extends the classical ACO paradigm to better deal with the p2p's dynamic nature. Focusing initially on the appearance of new resources in the network we have shown that it is possible to increase the efficiency of ant routing by a significant margin.