Development of an evaluation method using a combined cat swarm optimization algorithm

Abstract

The object of the study is complex dynamic objects with a hierarchical structure. The problem solved in the study is to increase the efficiency of decision-making while ensuring the given reliability. The subject of the study is the process of decision-making in management problems using an improved cat swarm optimization algorithm (CSO), an improved genetic algorithm and evolving artificial neural networks. The proposed method, due to additional and improved procedures, allows you: – to take into account the type of uncertainty of the initial data for setting CA for the local search procedure; – to implement adaptive strategies for finding food sources by CA; – to take into account the experience of the most authoritative CA while conducting local and global search; – to take into account the available computing resources of the state analysis system of complex dynamic objects and determine their required amount for involvement; – to take into account the CA search priority; – to determine the best CA using an improved genetic algorithm; – to conduct training of knowledge bases, which is carried out by training the synaptic weights of the artificial neural network, the type and parameters of the membership function, and architecture of individual elements and the architecture of the artificial neural network as a whole; – to avoid the local extremum problem by using the jump procedure. The proposed method was tested on the example of solving the problem of determining the composition of an operational group of troops (forces) and elements of its operational structure. An example of using the method showed an increase in the efficiency of data processing at the level of 14–19 % by using additional improved procedures. The proposed approach should be used to solve the problems of evaluating complex and dynamic processes characterized by a high degree of complexity