Comparison of Intelligent Optimization Methods in the Fractional Fourier Transform

There is no effective algorithm for extremum seeking in the fractional Fourier transform (FRFT) except for step-based searching technique which is quite time consuming especially when the precision is highly desired. This makes FRFT hard to be applied in practice. In order to resolve this problem, we succeed introducing some intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm, particle swarm optimization and chaos optimization algorithm into fractional Fourier transform. Based on simulation we compare intelligent optimization methods with step-based algorithm from mean and variance of estimated value, effects of sampling frequency, resolution of different LFM signals in fractional Fourier domain, computation efficiency and precision. Results show that the chaos optimization algorithm is most preferable considering all the above mentioned factors.