Intelligent Optimization Algorithms In Electromagnetic Engineering

In this paper, the general Stoer-Bulirsch (S-B) algorithm is employed in developing a new AFS scheme (S-B AFS ). Since the S-B algorithm is a recursive tabular method and requires no matrix inversion, it can process a large number of sampling data for obtaining a rational interpolation function without suffering from singularity problems. This attribute virtually leads the proposed AFS approach to an ultra broad-band interpolation with a single rational function. A practical broad-band example is given to demonstrate the effectiveness of the S-B AFS.Genetic algorithm is a form of evolutionary algorithm that uses a population of trial solutions to search the encoded space of interest. Through application of reproduction, mutation, competition, selection, and recombination operators, new solutions are generated which are then evaluated and the simulated evolution process iteratively repeated, generation by generation. In this paper, a genetic algorithm is implemented for phased array synthesis with a far-field sidelobe constraint, using seven variables only and fourteen variables then.Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational cost and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array .The result shows that particle swarm optimization performs better in this case, which implies that the two methods traverse the problem hyperspace differently. Finally, a new combination of particle swarm optimization and least-squares support vector machines technique is proposed to obtain a full FDTD time record from a relatively short time record, which proves a good performance.