(сетевой с №3-2015)
(сетевой с №2-2015)
|Articles and journals | Tariffs | Payments | Your profile|
Development of an approach operating with the triangular expression of fuzzy numbers based on the PSO-algorithm.
Abstract.The object of studies involves intellectual algorithm for solving optimization problems. It is known that for the same type of project procedures some cases require exact solutions, while others allow for approximate solutions. For this reason the issue of managing the exactness of the approximate solutions is so topical. An approximate solution may be regarded as some sphere of dots, each of them being a possible problem solution. It is supposed that at the early stages of solving optimization problems, it is possible to operate fuzzy ranges, while gradually narrowing the search area. The authors offer an approach, which complements the well-known algorithm of particle swarm optimization with the possibility to process fuzzy numbers with the triangular expression. The current multi-agent methods for the adaptive search for the optimization solutions are developed towards improvement of the interaction among the agents. For example, the well-known particle swarm optimization methods (PSO) is based upon the idea of population and it models the behavior of the birds in a flock or fish in a shoal. At the same time classic bio-inspired methods for finding solutions usually operate with clear solutions. The authors have developed the modification of the PSO algorithm thanks to performance of a number of known operations with the fuzzy numbers involving triangular expressions. The special feature of this approach is organization of the intellectual searching process in a fuzzy solution space. Its originality is due to the development of the method for the movement of an agent (group of agents) within the area formed with the triangular expression of fuzzy numbers. This approach allows for searching for solutions in fuzzy spaces, operating with the variables of the "close to X" type, avoiding the linguistic analysis.
Keywords: optimization, vague estimates, swarm optimization method, search area, fuzzy operations, evolutionary method, swarm optimization methods, fuzzy multitudes, adaptation, evolution method
Article was received:01-04-2017
This article written in Russian. You can find full text of article in Russian here .