The proposed in this work approach to managing dynamically changing systems is based on the use of optimization algorithms: random search algorithms with self-training and ant algorithms. In a dynamically changing system during its operation, the system status could change: the hardware configuration, the system load and the system functionality. The control problem is considered as the problem of unconditional optimization. Self-learning random search algorithms and ant algorithms, acting by the rule-of-thumb method, allow one to tune into the current status of the system. This is achieved by the entry into the algorithms the data about the success and failure of the previous steps.

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