980 research outputs found
Genetic Algorithm Optimization of a High-Directivity Microstrip Patch Antenna Having a Rectangular Profile
A single high-directivity microstrip patch antenna (MPA) having a rectangular profile, which can substitute a linear array is proposed. It is designed by using genetic algorithms with the advantage of not requiring a feeding network. The patch fits inside an area of 2.54λ x 0.25λ, resulting in a broadside pattern with a directivity of 12 dBi and a fractional impedance bandwidth of 4%. The antenna is fabricated and the measurements are in good agreement with the simulated results. The genetic MPA provides a similar directivity as linear arrays using a corporate or series feeding, with the advantage that the genetic MPA results in more bandwidth
Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling
Broadcasting antenna array null filling is a very
challenging problem for antenna design optimization. This paper
compares five antenna design optimization algorithms (Differential
Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive
Invasive Weed) as solutions to the antenna array null filling
problem. The algorithms compared are evolutionary algorithms
which use mechanisms inspired by biological evolution, such as
reproduction, mutation, recombination, and selection. The focus of
the comparison is given to the algorithm with the best results,
nevertheless, it becomes obvious that the algorithm which produces
the best fitness (Invasive Weed Optimization) requires very
substantial computational resources due to its random search
nature
Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays
Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm
In this study, the peak side lobe level (PSLL) in the radiation pattern of a linear antenna array (LAA) is lowered without affecting its first null beam width (FNBW). Antenna array synthesis is commonly applied to achieve high directivity, low side lobes, high gain and desired null positions in the output radiation pattern. But output parameters like PSLL, null positions and beam width conflict with each other, i.e. as one parameter improves, the other deteriorates. To avoid this problem, a multi-objective optimization algorithm can be implemented, in which both the conflicting parameters can be simultaneously optimized. This work proposes a multi-objective algorithm, which takes advantages of the well-known Chebyshev tapering and genetic algorithm (GA), to lower the PSLL without broadening the beam further. Array elements are fed using Chebyshev tapered excitations while GA is incorporated to optimize the elemental spacing. The results of 28-element LAA are compared with those of multi-objective Cauchy mutated cat swarm optimization (MO-CMCSO) existing in literature, which has also been proven to be superior to multi-objective cat swarm optimization (MO-CSO) and multi-objective particle swarm optimization (MO-PSO). Results indicate that the proposed algorithm performs better by further reducing the PSLL from -21.57 dB (MO-CMCSO) to -28.18 dB, while maintaining the same FNBW of 7.4 degrees
Source localization within a uniform circular sensor array
Traditional source localization problems have been considered with linear and planar antenna arrays. In this research work, we assume that the sources are located within a uniformly spaced circular sensor array. Using a modified Metropolis algorithm and Polak-RibiÚre conjugate gradients, a hybrid optimization algorithm is proposed to localize sources within a two dimensional uniform circular sensor array, which suffers from far field attenuation. The developed algorithm is capable of accurately locating the position of a single, stationary source within 1% of a wavelength and 1° of angular displacement. In the single stationary source case, the simulated Cramer-Rao Lower Bound has also shown low noise susceptibility for a reasonable signal to noise ratio. Additionally, the localization of multiple stationary sources within the array is presented and tracking capabilities for a slowly moving non-stationary source is also demonstrated. In each case, results are presented, analyzed and discussed. Furthermore, the proposed algorithm has also been validated through hardware experimentation. The design and construction of four microstrip patch antennas and a wire antenna have been completed to emulate a circular sensor array and the enclosed source, respectively. Within this array, data has been collected at the four sensors from several fixed source positions and fitted into the proposed algorithm for source localization. The convergence of the algorithm with both simulated data and data collected from hardware are compared and sources of error and potential improvements are proposed
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