Michal Przewozniczek

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The major objective of this paper is to deploy an effective evolutionary algorithm (EA) for the congestion problem in connection-oriented networks. The network flow is modeled as non-bifurcated multicommodity flow. The main novelty of this work is that the proposed evolutionary algorithm consists of two levels. The high level applies typical EA operators.(More)
One of the main evolutionary algorithms bottlenecks is the significant effectiveness dropdown caused by increasing number of genes necessary for coding the problem solution. In this paper, we present a multi population pattern searching algorithm (MuPPetS), which is supposed to be an answer to situations where long coded individuals are a must. MuPPetS uses(More)
In this paper we address the problem of working paths optimization in survivable MPLS network. We focus on an existing facility network, in which only network flows can be optimized to provide network survivability using the local repair strategy. The main goal of our work is to develop an effective evolutionary algorithm (EA) for considered optimization(More)
The fast social and economic development observed in the recent years brings up new challenging optimization problems. These problems are often very hard not only because of their computational complexity, but also due to their enormous solution space size. Therefore, this paper proposes an effective optimization method, based on the novel Multi Population(More)
The new promising technologies in the network communication field bring the possibilities of faster and more safe information sending. However, the new technologies bring up new computation problems as well. The network flow may be organized in a better way with respect to different criteria. It is common that such problems are hard not only because of(More)