An improved method of finding solutions based on the cuckoo algorithm is proposed. The research object is the decision-making support systems. The research subject is the decision making process in management tasks using artificial intelligence methods. The hypothesis of the research is to increase the efficiency of decision making with a given assessment reliability. The proposed method is based on a combination of the cuckoo algorithm and evolving artificial neural networks. The method has the following differences: ‒ an additional processing of the source data takes place taking into account the uncertainty about the state of the control objects and the type of data noise about the state of the control object is additionally taken into account; ‒ the state model of the control object is adjusted taking into account the available computing resources of the system; ‒ added procedures to reduce the probability of detecting nests and reducing the length of the cuckoo’s step; ‒ knowledge bases about management objects are additionally taught. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The effectiveness of the proposed method was evaluated and it was established that the proposed modification provides a better value of the objective function compared to the results obtained by other authors and ensures the fulfillment of all restrictions. The specified example showed an increase in the efficiency of data processing at the level of 21–28 % due to the use of additional improved procedures. It is advisable to use the proposed method in decision making support systems of automated control systems.

The object of the study is decision support systems. A methodology for evaluating information and analytical support in decision support systems was developed. The method consists of the main stages: assessment of the type of uncertainty about the state of the analysis object, calculation of criteria and determination of development options, determination of system reaction time, formation of the initial scenario. The next steps are establishing the target state of the object, analyzing options for influencing the analysis object, obtaining intermediate target states of the analysis object, and determining options for the development of the analysis object. The method was developed because of the need to process more information and has a moderate computational complexity. It was found that the proposed method has a computational complexity of 10–15 % lower compared to the methods for evaluating the effectiveness of management decisions. This method will allow assessing the state of information and analytical support and determining effective measures to increase efficiency. The method will allow analyzing possible options for the development of the assessment object in each development phase and the moments in time when it is necessary to carry out structural changes that ensure the transition to the next phase. In this case, subjective factors of choice are taken into account while searching for solutions, which are formalized in the form of weights for the components of the integral efficiency criterion. The maximization of the criteria, calculated taking into account the preferences, makes it possible to determine the best option for the development of the assessment object. The method allows increasing the speed of assessment of the state of information and analytical support, reducing the use of computing resources of decision support systems, developing measures aimed at increasing the efficiency of information and analytical support