Hybrid Bionic Algorithms for Solving Problems of Parametric Optimization

Parametric optimization tasks are currently being used in various application areas. These tasks may include weather forecasting weather station, the calculation of the parameters of electric motors, search weights in the neural network. This paper presents a hybrid bionic algorithm for solving the problems of parametric optimization. The idea of the algorithm is to use the migration operator to find quasi-optimal solutions in the use of the modified Ant colony algorithm to dynamically control unpromising populations obtained in course of algorithm. Also, a series of experiments, which were confirmed by theoretical estimates, identified the optimal parameters of the algorithm. The time complexity of the algorithm was O (n ). The value of the time offset, the 4 quality of the solutions obtained via hybrid heuristics for a large number of input parameters. Thus, in the course of the experiments, the number of input parameters for 100 or more a hybrid algorithm never got into a local optimum and the solution found was approached or equal to the global.