|Article title||PARAMETRIC OPTIMIZATION ALGORITHM BASED ON THE MODEL OF GLOWWORM SWARM BEHAVIOR|
|Authors||V.V. Kureichik, D.V. Zaruba, D.Y. Zaporozgets|
|Section||SECTION I. EVOLUTIONARY MODELLING, GENETIC AND BIONIC ALGORITHMS|
|Month, Year||06, 2015 @en|
|Abstract||Currently, the problem of parametric optimization are widely used in various fields of science and technology. These problems include weather forecasting, the calculation of various parameters of electric motors, as well as finding the weight coefficients in the neural network. The problem relates to the class of NP-hard and is not deterministic algorithms to solve it. Therefore, the development of promising heuristic methods to obtain quasi-optimal solutions in polynomial time. The article deals with the problem of parametric optimization of technical objects. From a mathematical point of view the process of parametric optimization is reduced to the problem of global conventional continuous optimization. Shows the formulation of the problem of parametric optimization. To solve this problem developed a stochastic algorithm based on the model of behavior of the swarm of fireflies. Roy is regarded as a multi-agent system where each agent operates autonomously on a fairly primitive rules. The basis of the behavior of a swarm of self-organization, ensuring the achievement of common objectives based on a swarm of low-level interaction. Roy does not have a centralized management. Its features include direct and indirect exchange of information between local individuals. The set of relatively simple agents strategy builds its behavior without global governance. The developed method is based on creating a dynamic decision-making of each agent that is guaranteed to find all the local optima of the objective function in polynomial time. For the analysis of this method, a series of experiments. The data obtained confirmed the theoretical evaluation, and possible to establish the optimal parameters of the algorithm. Conducted a series of tests and experiments have shown promising application of this approach.|
|Keywords||Parametric optimization; swarm optimization methods; bioinspired search.|
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