In this paper, a new chaotic teaching learning based optimization (CTLBO) is proposed. TLBO is a rather newly proposed population-based algorithm. This algorithm has no control parameters for the tuning and has a simple structure. We improve its performance by chaotic maps. First, the presented CTLBO is tested on nine unimodal/multimodal benchmark functions. Then, chaotic sequences are applied as vectors with different initial values for design of a frequency reconfigurable antenna (FRA) as a practical example. Comparisons of the performance of this algorithm with those of the basic TLBO, genetic algorithm and particle swarm optimization show the ability of this algorithm in design of FRAs in terms of faster convergence and better performance. A prototype of the optimized antenna with CTLBO algorithm is fabricated and the simulation and measurement results agree suitably.
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