Analysis of optimisation and decision-making theories was made during this master degree thesis. The study of the optimisation tasks types and their solving methods was made. Main focus was given to the solutions of multiple criteria optimization problems. Computing software MINIMUM and existing software for the solution of multiple criteria optimization tasks were analyzed during the study. Applying computing software MINIMUM strategy for the solution of multiple criteria optimization tasks has been developed and special software for the efficiency research created. Experimental trials have been carried out in order to investigate and compare efficiency of the existing and specially created software for the solution of multiple criteria optimization tasks. Using specially created software, experimental solutions of multiple criteria optimisation tasks in graphical environment showed that optimal solution is found faster i. e. duration of the optimum result computing time was most economical. New programming language PowerBuilder was also analyzed during the development of the special software for the efficiency research and more experience in modern software development was gained.

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.