COMPARATIVE ANALYSIS OF OPTIMIZATION METHODS IN THE INVESTIGATION OF A WEIGHMEASURING SYSTEM AND THERMOREGULATOR
Науковий журнал «Радіоелектроніка, інформатика, управління»
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COMPARATIVE ANALYSIS OF OPTIMIZATION METHODS IN THE INVESTIGATION OF A WEIGHMEASURING SYSTEM AND THERMOREGULATOR |
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Koshevoy, N. D.; National Aerospace University named after M. E. Zhukovskoho “HAI”, Kharkiv, Ukraine. Kostenko, E. M.; Poltava State Agrarian Academy, Poltava, Ukraine Beliaieva, A. A.; National Aerospace University named after M. E. Zhukovskoho “HAI”, Kharkiv, Ukraine. |
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Context. For the first time, the use of taboo-search methods, random search, a swarm of particles for the construction of costeffective<br />experiment plans for the study of a weighing system and a temperature regulator was proposed.<br />Objective – to carry out a comparative analysis of the developed optimization methods, such as taboo search, random search,<br />particle swarm when searching for the optimal plans for the experiment during the study of the weighing system and thermostat.<br />Method. Methods for constructing the experimentally optimal implementation matrix for the experiment using algorithms of a<br />swarm of particles, taboo search and random search are proposed. In the beginning, the number of factors and cost of transitions for<br />each level of factors is introduced. Then, taking into account the input data, the initial experimental design matrix is formed. When<br />using the taboo search algorithm at each iteration step, the best solution in the neighborhood of the current solution is chosen as the<br />new current solution and the check is made whether it is in the taboo list. Thus, calculations occur until the algorithm reaches the<br />specified number of iterations. The list of taboos is formed from decisions that have a minimum cost. The random search method is<br />based on permuting the columns of the planning matrix. The number of iterations of the algorithm is specified by the user. The<br />method of the particle swarm is based on modeling the behavior of the particle population. At each point where the particle visited,<br />the value of the experiment is calculated. In this case, each particle remembers which (and where) the best value of the cost of the<br />experiment, she personally found and where the point is located, which is the best among all the points that explored the particles. At<br />each iteration, the particles correct their velocity (modulus and direction). After a certain number of iterations, the particles are collected<br />near the best point. Then, among all the new points, we check whether we have found a new globally better point, and if<br />found, remember its coordinates and the value of the cost of conducting the experiment in it. Then the gain is calculated in comparison<br />with the initial cost of the experiment.<br />Results. The software that implements the proposed methods was developed, which was used to conduct computational experiments<br />to study the properties of these methods in the study of a weighing system and a temperature regulator. Optimized for the cost<br />of implementation of the experiment plans were synthesized, as well as the gains in optimization results as compared to the initial<br />and maximum costs of the experiment.<br />Conclusions. The conducted experiments confirmed the efficiency of the proposed methods and the software that implements<br />them, and also allow them to be recommended for application in practice when constructing optimal experimental design matrices. |
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Zaporizhzhya National Technical University 2019-01-18 11:02:38 |
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application/pdf http://ric.zntu.edu.ua/article/view/154605 |
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Radio Electronics, Computer Science, Control; No 4 (2018): Radio Electronics, Computer Science, Control |
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ru |
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Copyright (c) 2019 N. D. Koshevoy, E. M. Kostenko, A. A. Beliaieva |
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