Application of population algorithms in the problems of multiobjective optimization of electrical filters characteristics

Population algorithms enable simultaneously search many elements of approximation of Pareto optimal decisions set and hereupon provide large advantage in consumption of time compare to scalar goal function method that found a single decision in the search cycle. The capability of open-source platform PlatEMO for solving of problems of multiobjective optimization of electrical filters characteristics was investigated in this work. Experience has shown that for two-objectives optimization problems only 6 algorithms of 71 provided good results. Approximations of Pareto set found by these algorithms were better than approximation found by scalar goal function method. Comparison was carried out by means of Coverage indicator that estimates the part of the first approximation elements dominated by the second approximation elements. For three-objectives optimization problems only two algorithms provided acceptable results. In this case approximations of Pareto set found by population algorithms were worse than that found by scalar goal function method. The conclusion was made that a rational method may consist of application of population algorithm for the solving of several two-objective optimization problems with constrains on other objectives and successive aggregation of found subsets.

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