Improvement of the optimization method based on the wolf flock algorithm
Keywords:artificial intelligence, wolf flock algorithm, data uncertainty, evaluation efficiency, adaptability
The problem that is solved in the research is to increase the efficiency of decision making in management tasks while ensuring the given reliability, regardless of the hierarchical nature of the object. The object of the research is decision making support system. The subject of the research is the decision making process in management tasks using an improved wolf flock algorithm. The hypothesis of the research is to increase the efficiency of decision making with a given assessment reliability. In the course of the research, an improved optimization method based on an improved wolf flock algorithm was proposed. In the course of the conducted research, the general provisions of the theory of artificial intelligence were used to solve the problem of analyzing the objects state and subsequent parametric management in intelligent decision making support systems.
The essence of the improvement lies in the use of the following procedures, which improve basic procedures of the wolf flock algorithm, namely search and chase:
– taking into account the type of uncertainty of the initial data while constructing the wolf flock path metric;
– searching for a solution in several directions using individuals from the wolf flock;
– initial presentation of individuals from the wolf flock;
– an improved procedure for adapting a flock of wolves;
– taking into account the available computing resources while choosing the number of leaders in a flock of wolves.
An example of the use of the proposed method is presented on the example of assessing the state of the operational situation of a group of troops (forces). The specified example showed an increase in the efficiency of data processing at the level of 23–30 % due to the use of additional improved procedures
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Copyright (c) 2023 Oleksandr Trotsko, Nadiia Protas, Elena Odarushchenko, Yuliia Vakulenko, Larisa Degtyareva, Viktor Parzhnytskyi, Pavlo Khomenko, Leonid Kolodiichuk, Vitaliy Nechyporuk, Nataliia Apenko
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