Krzysztof Schiff

In this article, ant colony optimisation algorithms for the triple matching problem are described. This is the
first elaborated ant algorithm for this problem. The problem is modeled by means of a 3-dimensional array. The ant algorithm was compared with the Apx3Dmatchnig-F algorithm and tested for different values of ant algorithm parameters. The results of these tests were presented and discussed.

The maximum clique problem is a very well-known NP-complete problem and for such a problem, meta-heuristic algorithms have been developed which ant algorithms belongs to. There are many algorithms including ant algorithms that have been elaborated for this problem. In this paper, a new dynamic function of selecting with a new improvement procedure in order to get a larger size of clique for the ant algorithm is presented and this search for the maximum clique in graph is compared to the bestn ant algorithms that are already known.

The clustering problem is one of the main problems which can be encountered in a data analysis. This problem can be modelled by means of a graph; finding clusters means finding cliques in the graph. Often there is a need to find clusters (cliques) in a graph in different ways and to construct a list of clusters. This paper describes two such ways, these can be stated as the cluster minimum covering problem and the vertex cluster minimum partitioning problem. This paper describes new ant algorithms which were used in order to make a list of clusters in both presented problems, and also discusses the results of their comparison.

This article describes a new ant colony optimisation algorithm for the facility localisation problem with a new heuristic pattern proposed by the author, which consists of three parts: the function of the average cost of client servicing; the total minimum cost of servicing from a site, which is selected and included into the solution; the function of improving the cost of already serviced clients. In this comparison, simulations were presented, and two parameters were observed: the number of sites and the cost of client servicing. The new algorithm allowed to improve the solution in both of these parameters.

This article describes a new ant colony optimisation algorithm for the discrete knapsack problem with a new heuristic pattern, based on the ratio of the square of the profit coefficient to the square of the weight coefficient of the original problem. This new heuristic is used in order to choose objects that should be packed into the knapsack. This pattern was compared with two used in ant algorithms and which have been presented in the literature on the subject of ant colony optimisation algorithms for the 0-1 Knapsack Problem. The two other patterns are based on the ratio of the profit coefficient to the weight coefficient multiplied respectively by the total and the current knapsack load capacity. Results of tests under a width range of ant algorithm parameters such as the number of cycles, the number of ants, the evaporation rate, and the load knapsack capacity are shown and discussed.

Algorytm mrówkowy dla zagadnienia pokrycia zbioru

W artykule przedstawiono nowy hybrydowy algorytm mrówkowy dla problemu zagadnienia pokrycia zbioru o minimalnym koszcie. Problem jest zamodelowany za pomocą grafu dwudzielnego. W modyfikowanym algorytmie wprowadzono nową heurystykę wyboru wierzchołków do podzbioru wierzchołków pokrywających. Opracowany algorytm przetestowano i porównano, a wyniki tych badań omówiono