Аннотация:
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
