Implementation of the gravity search method for optimization by cost expenses of plans for multifactorial experiments

Abstract

One of the main ways to improve the efficiency of experimental research is the use of methods for planning experiments. At the same time, experiment planning can significantly reduce the amount of experimental research by reducing the number of experiments, as well as improve the accuracy and reliability of the results obtained. It is characteristic that the experiments in terms of experiment are not equivalent, that is, their implementation requires different material and time costs. In this regard, the problem arises of optimizing the plans of multivariate experiments in terms of cost or time costs. This is especially important when studying valuable and long-term processes. To solve the problems of optimizing plans for multifactorial experiments in terms of cost (time) costs, it is necessary to develop effective methods for finding optimal plans and their software. Existing methods for optimizing experimental plans are characterized by such shortcomings as low speed, a limited number of studied object factors, and the exact solution is not always found. This article explores the method of gravitational search for the optimal cost (time) cost plan for multifactorial experiments. The method uses the analogy of the motion of solid bodies due to their gravitational interaction. In this case, the rows of the experiment planning matrix are considered as such solid bodies, which are placed in it depending on the decrease in the cost of transitions between rows (gravity). An algorithm and software have been developed that implement the proposed method. The program is presented in the algorithmic language Python. On a number of examples for the study of technological processes, the efficiency and effectiveness of the method of gravitational search for optimal cost (time) costs of plans for multifactor experiments has been proved. The object of the research: processes of optimization of plans of multifactorial experiments according to cost (time) costs. The subject of the study: the method of gravitational search for the optimal cost (time) plans of multifactorial experiments and the software implementing it.