Abstract. Queueing theory provides methods for analysis of complex service systems in computer systems, communications, transportation networks and manufacturing. It incorporates Markovian systems with exponential service times and a Poisson arrival process. Two queueing systems with losses are also briefly characterized. The article describes firefly algorithm, which is successfully used for optimization of these queueing systems. The results of experiments performed for selected queueing systems have been also presented.

Abstract:In transport planning, one should allow passengers to travel through the complicated transportation scheme with efficient use of different modes of transport. In this paper, we propose the use of a cockroach swarm optimization algorithm for determining paths with the shortest travel time. In our approach, this algorithm has been modified to work with the time-expanded model. Therefore, we present how the algorithm has to be adapted to this model, including correctly creating solutions and defining steps and movement in the search space. By introducing the proposed modifications, we are able to solve journey planning. The results have shown that the performance of our approach, in terms of converging to the best solutions, is satisfactory. Moreover, we have compared our results with Dijkstra's algorithm and a particle swarm optimization algorithm.

Abstract. This paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NPhard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined. The QAP is an NP-hard problem and this difficulty is not restricted only to finding the optimal solution. Sahni and Gonzalez [4] proved that even finding an ɛ-approximation solution for QAP is a hard problem in this sense that the existence of a ɛ-approximation algorithm implies P = NP.Finding an optimal solution to QAP is a difficult task not only in case of looking for the best solution among all the feasible ones. It might appear that finding an optimal solution in the subset of the feasible solutions can be easier. For QAP it was proven that finding an optimal solution in case of the local search is a difficult problem too. Johnson and Papadimitriou in [6] created the base for the complexity theory in the local search case, where a special structure of neighbourhood is introduced. They define the PLS-problems (polynomial-time local search problem) as a set for which a locally optimal solution can be found in polynomial time. Next, they introduce a PLS-complete decision problem as an analogy of NP-complete one, which are the most difficult problems in PLS.Murthy, Pardalos and Li [7] proposed a neighbourhood structure for QAP problem and proved that the corresponding local search problem is PLS-complete. The proposed structure is similar to that proposed by Kernighan and Lin [8] for the graph partitioning problem called K-L type neighbourhood structure N K-L . As the problem of finding QAP optimal solution in N K-L (called (QAP, N K-L )) is PLS-complete, then in the

The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the algorithm has to be adapted to this problem, including crossover and mutation operators and introducing quantum principles in particular procedures. The results have shown that the performance of our approach in terms of converging to the best solutions is satisfactory. Moreover, we have presented the results of the selected parameters of the approach on the quality of the obtained solutions.

The design of a methodology for the effective scene understanding systems is one of the main goals of the researchers in the analysis of video surveillance. The objects in the scene have to be identified. Hence, it is necessary to detect the parts belonging to the background. In the article we introduce the base algorithms, which enable us to realization of scenarios. We briefly describe base algorithms (object detection, object localization, recognition of humans, movement detection and configuration of scene) used in three selected scenarios: violation of protected zones, abandoned objects and vandalism (graffiti). These scenarios were tested on several films, obtained from Internet and made by participants of project SIMPOZ. The results of our experiments are presented. The basic algorithms for detecting and locating objects are very quickly, but movement detection ("optical flow") and recognition of humans algorithms work longer.