Abstract
Today parametric optimization problems are widely used in different science and technology domains. These problems are weather forecasting, calculation of electromotor parameters as well as VLSI design problems which belong to NP-problems and do not have deterministic algorithms to solve it. So, it is necessary to develop up-and-coming heuristic methods to obtain quazi-optimal solutions during polynomial time. The paper deals with parametric optimization of technical objects. From the mathematical point of view the parametric optimization problem reduces to the global constrained continuous optimization. The formulation of parametric optimization problem is made. To solve this problem it is developed a stochastic algorithm based on foraging behavior of E.coli bacteria. A bacterial colony is considered as multiagent system in which each agent operates in autonomy according with quite elementary rules. Colony behavior is based on self-organization to reach common goals by low-level interconnection. The colony does not have centralized control. Conjunction of simple agents creates a behavioral strategy without any global control. To analyze the developed algorithm there were carried out a set of experiments, which confirm theoretical estimations and calculate optimal values of algorithm parameters. Experimental results show perspective of this approach.
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This research is supported by the Council for Grants (under RF President), the project #MК-92.2017.8.
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Zaruba, D., Zaporozhets, D., Kuliev, E. (2017). Parametric Optimization Based on Bacterial Foraging Optimization. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_6
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DOI: https://doi.org/10.1007/978-3-319-57261-1_6
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