This paper presents a comparative study that was done using genetic algorithm, improved particle swarm optimization and the hybrid technique genetical swarm optimizer approaches for the design of one-dimensional photonic crystal selective filters. The three evolutionary methods for synthesizing the geometrical parameters of a fiber Bragg grating structure from its layer thicknesses are proposed and demonstrated. The synthesis of the mono-band 1-D PhC selective filters is designed as a mono-objective problem, and these 1-D PhCs are composed of alternate Si and Air layers with thicknesses on the micron scale. The main contribution of this paper is formed by the solution to this kind of problems. According to the literature, this hybrid methodology genetical swarm optimizer has not been dealt with before, when 1-D PhCs selective filters are considered. Comparison of the GA, IPSO and GSO for the selected set of examples revealed an improvement of paramount importance in terms of error lowering and the number of iteration cycles diminution.
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