Development of a solution search method using an improved monkey algorithm - Shyshatskyi, Andrii; Nechyporuk, Olena; Kuchuk, Nina; Stanovska, Iraida; Nalapko, Oleksii; Shknai, Oleh; Protas, Nadiia; Shostak, Serhii; Binkovska, Anzhela; Shapoval, Petro (Eastern-European Journal of Enterprise Technologies, 2023)
The object of the research is decision support systems. The subject of the research is the decision-making process in management problems using the monkey algorithm and evolving artificial neural networks. A solution search method using an improved monkey algorithm is proposed. The research is based on the monkey algorithm – for finding a solution regarding the state of an object. For training monkey agents (MA), evolving artificial neural networks are used. The method has the following sequence of steps: – input of initial data; – processing of initial data taking into account the degree of uncertainty; – a search vector is generated for each MA, taking into account the degree of uncertainty; – determination of the initial speed of MA movement; – calculation of the fitness function of the MA solution; – calculation of the height of MA movement; – verification of fulfillment of local jump conditions; – generation of local search plane coordinates; – calculation of the fitness function of the MA solution; – generation of global search plane coordinates; – search distribution among the MA flock; – changing the speed of MA movement; – checking the permissible value of the obtained solution regarding the object state; – training of MA knowledge bases. The originality of the proposed method lies in the arrangement of MA taking into account the uncertainty of the initial data, improved procedures of global and local search taking into account the degree of noise of data about the state of the analysis object. A feature of the proposed method is the use of an improved MA training procedure. The training procedure consists in learning the parameters and architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 23–28 % due to the use of additional improved procedures