Skip to main content

Stochastic methods for combinatorial optimization

Final Activity Report Summary - STOCHASTIC METHODS (Stochastic methods for combinatorial optimization)

The aim of the project was to study stochastic methods for combinatorial optimisation problems. The work performed can be divided in two stages: theoretical and practical. A modification of ant algorithm has been made. Convergence of the new algorithm to the global optimum has proved. Some research on application of ant algorithm in Multiple knapsack problem (MKP) is done. We studied two pheromone models: pheromone on the nodes of the graph of the problem and pheromone on the arcs of the graph of the problem. The MKP is a constrain problem and gives a lot of possibilities to construct heuristic information.

Various types of heuristic information, static and dynamic, have been constructed. Other interesting problem we worked on is GPS network surveying. We applied several stochastic methods and then analysed the achieved results. GRID computing is a form of distributed computing. The problem that we attacked is tasks scheduling on available computing resources. The algorithm is based on ant method and is applied in dynamical way. The achieved results have presented on several international conferences and have reported on some seminars. They have published on conference proceedings and a book chapter.