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.

Abstract: The object of research is the model of artificial immune system. Subject of the research is providing a method of constructing a fuzzy decoder. The authors proposed to use fuzzy membership function as the decoders. This functions describes the relevance of a controlled parameter to a critical situation. Using such an approach based on fuzzy decoders allows to move from binary quantitative classification to fuzzy qualitative estimates. The article present an example o f construction of a decoder for fuzzy term “semiperimeter length of L, describing a fragment of the designed product, should be no more than 0.7 nm”. On the basis of the function CON(μ1(L)), describing fuzzy matching condition “very close to 0.7 nm” the authors build a function μ5(L), describing fuzzy matching condition “a little less than 0.7 nm”. Fuzzy decoder for conformity assessment interval is based on the given interval membership function. The authors give a graph of a μ7 decoder  function semiperimeter on the length L, describing the belonging to “semiperimeter desired length from 0.55 to 0.7 nm” condition. By analogy with the conditions “very close to 0.7 nm” and “slightly close to 0.7 nm” it is possible to determine a membership functions “very in range from 0.55 to 0.7 nm” and “slightly in range from 0.55 to 0.7 nm”. The research method is based on the construction of fuzzy decoders describing the undesirable state of the computational process. Fuzziness is described by the membership function. The novelty of the research is in getting fuzzy decoders receptive to fuzzy commands. Using the corresponding fuzzy membership function μ decoder it is possible adjust the process of estimating the degree of closeness of the controlled parameter to a critical situation. Applying CON and DIL functions to the decoder functions allows to change their susceptibility on test data from 20-30% up to 200% -300%.