Testing the Algorithm of Area Optimization by Binary Classification with Use of Three State 2D Cellular Automata in Layers

The paper is dedicated to a new algorithm of optimization in the sense of the area. Proposed method joins a few issues. First one is utilizing data from the set of sensors monitoring the area put into optimization. The second one is using the classification method based on two-dimensional three-state cellular automata, working on the data reported by the sensors. This method classifies all points of the area based on the data received from the sensors and designates optimal subarea. The third issue is applying the categorization layers to the data received from sensors. Such, approach gives a possibility to specify the areas in the different levels and, in consequence, after analysis, optimal subarea or subarea including the optimal point can be designated. This method can be used in different optimization tasks, starting from simple one as optimization of n- dimensional function, through specifying the contaminated area utilizing data from mobile sensors and finally estimating the contamination source-term. In this paper are presented results of testing for the proposed algorithm on a few selected functions from the set of dedicated for this purpose.