Abstract
Genetic algorithm (GA) is a popular optimization technique used in the design of performance improved microstrip patch antennas (MPAs). The fitness function plays a vital role in the successful application of GA in MPAs. This paper investigates the performance of several fitness functions for achieving bandwidth improved MPAs.
Highlights
Among the performance improvement techniques, genetic algorithm (GA), which is a powerful optimization technique, has shown to be useful in a wide area of electromagnetics [1,2,3,4]
Genetic algorithm (GA) is a popular optimization technique used in the design of performance improved microstrip patch antennas (MPAs)
This paper investigates the performance of several fitness functions for achieving bandwidth improved MPAs
Summary
Among the performance improvement techniques, genetic algorithm (GA), which is a powerful optimization technique, has shown to be useful in a wide area of electromagnetics [1,2,3,4]. GA has been used to enhance the performance of microstrip patch antennas (MPAs) by optimizing the bandwidth, resonating frequency, directivity, gain, size etc. When there are several current paths on the patch, MPAs show multiband [5,6,7,8,9,10,11] performance. The appropriate formulation of the fitness function is very important in the success of optimization procedure It serves as the link between the physical problem being optimized and GA. Summation of reflection coefficient values over a range of frequencies is another fitness function presented in the literature [24]. Several fitness functions are compared and a new one is introduced in order to obtain a broadband MPA. Five MPAs are optimized by using these fitness functions and their bandwidth performances are compared.
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