Hierarchical Neuro-Game Model of the FANET Based Remote Monitoring System Resources Balancing

A Hierarchical Model of a Vector Nash Equilibrium Search in a Control Problem under Conflict and Uncertainty

“…In (6), it is required to minimize the vector criteria components i (u, z) on the set (u U i ) under uncertainty z ∈ Z. To solve problem (6), the vector minimax principle and the coevolutionary MOU algorithm are used [4,5].…”

“…The technology of the neuroevolutionary synthesis of algorithms for multi-object multicriteria systems' (MMS) control under conflict and uncertainty is based on the development and widespread use of coevolutionary algorithms for finding a game's stable-effective compromises (STEC) [1][2][3] and coordinated STEC (COSTEC) [4][5][6]. However, these coevolutionary algorithms have extremely high computational complexity.…”

Artificial Neural Networks Multicriteria Training Based on Graphics Processors

“…To solve problem ( 1), ( 2), the hierarchical evolutionary algorithm (HEA) of the MCOU developed in [6,7] is used. As studies [5,6] show, the HEA MCOU, when used in ANN training tasks, shows a high computational complexity. Therefore, it is proposed to implement the HEA software for solving problem (1), ( 2), based on the GPU architecture and OpenCL technology.…”

“…Currently, the technology of the neuroevolutionary synthesis of management and decision-making models is being intensively developed, which is considered as a promising means of implementing intelligent algorithms for analyzing information and management under conflict and uncertainty in real time [1][2][3][4][5]. The effectiveness of the neuroevolutionary approach for solving this class of problems is determined by the ability to take into account uncertain factors, such as conflict uncertainty, the multicriteria of management goals, and the uncertainty of environmental conditions.…”

Neuro-Evolutionary Synthesis of Game Models of Control under Uncertainty Based on Distributed Computing Technology

“…In [19][20][21], an evolutionary computational technology is being developed that provides the possibility of combining various game-theoretic principles of optimality and takes into account various uncertain factors on a single conceptual and algorithmic basis in the task of MMS control optimization under conflict and uncertainty. This technology is implemented in the form of a library of evolutionary algorithms [20] and has been used to solve practical problems of the evolutionary synthesis of neuro-game algorithms of MMS control in real time based on STECU [22][23][24]. The analysis of the results allows us to draw the following main conclusions: the neuro-evolutionary technology of multicriteria control algorithms under conflict and uncertainty synthesis is effective; at the same time, the algorithms of game MMS control models neuro-evolutionary synthesis have extremely high computational complexity; the practical use of neuro-evolutionary technology for solving problems of the specified class requires its implementation on high-performance distributed computing architectures.…”