Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given

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

Abstract. This paper presents a Particle Swarm Optimization (PSO) algorithm for the Quadratic Assignment Problem (QAP) implemented on OpenCL platform. Motivations to our work were twofold: firstly we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly we planned to check, if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. We were specifically targeting low-cost popular devices, with limited capabilities. The paper discusses the algorithm and its parallel implementation, as well as reports results of tests.

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