So to putthis into perspective, extensive modeling with AOP (antenna optimizer,professional) shows that linear loading designs using decent diameter loadingcomponent work very well and are very efficient. Coil loading using wire sizeand fabrication techniques that maintain a Q of at least 300 works very welland are very efficient.
The evaluation of the directivity in different directions corresponding to the angular region defined for maximum radiation as well as maximum sidelobe and the backlobe level is given in the function calculate_objectives available within yagi_objective_function_direct.
The optimized Yagi-Uda antenna achieves a forward directivity of 9.48 dBi, which translates to 7.4 dBd (relative to a dipole). This result is a bit less than the gain value reported by the datasheet in reference [2] (8.5 dBd). The front-to-back ratio is 73 dB; this is part of the quantity that the optimizer maximizes. The optimized Yagi-Uda antenna has an E-plane beamwidth of 56 deg, which is the same as the datasheet. The H-plane beamwidth of the optimized Yagi-Uda antenna is 72 deg, whereas the value on the datasheet is 63 deg. The example does not address impedance matching over the band.
May be you will optimize the length and position of the director of your 2 m yagi (only one director available). The yagi boom should go along x direction and the hight over ground (z direction) should be variable. X = 0 may define the position of the reflector.
Design of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the accumulated cost of multiple antenna evaluations. This problem is especially pronounced in the case of global search, frequently performed using nature-inspired algorithms, known for poor computational efficiency. At the same time, global optimization is often required, either due to multimodality of the design task or the lack of sufficiently good starting point. A workaround is to combine metaheuristics with surrogate modeling methods, yet a construction of reliable metamodels over broad ranges of antenna parameters is challenging. This work introduces a novel procedure for global optimization of antenna structures. Our methodology involves a simplex-based automated search performed at the level of approximated operating and performance figures of the structure at hand. The presented approach capitalizes on weakly-nonlinear dependence between the operating figures and antenna geometry parameters, as well as computationally cheap design updates, only requiring a single EM analysis per iteration. Formal convergence of the algorithm is guaranteed by implementing the automated decision-making procedure for reducing the simplex size upon detecting the lack of objective function improvement. The global optimization stage is succeeded by gradient-based parameter refinement. The proposed procedure has been validated using four microstrip antenna structures. Multiple independent runs and statistical analysis of the results have been carried out in order to corroborate global search capability. Satisfactory outcome obtained for all instances, and low average computational cost of only 120 EM antenna simulations, demonstrate superior efficacy of our algorithm, also in comparison with both local optimizers and nature-inspired procedures.
This part of the paper discusses validation of the antenna optimization algorithm introduced in the previous section. It is based on four microstrip devices, including dual- and triple-band structures, as well as a quasi-Yagi antenna. The numerical experiments aim to verify the global search capability of out framework, as well as to compare it with benchmark methods, specifically, multiple-start local optimization and nature-inspired routines. The latter is represented by a particle swarm optimizer (PSO)95, which is arguably the most widely utilized population-based algorithm nowadays. The primary factors that are of interest are the optimization process reliability, quality of the design produced in the course of optimization, but also computational cost.
We propose a competitive binary multi-objective grey wolf optimizer (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective antenna topology optimization problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary multi-objective grey wolf optimizer (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex antenna topology optimization problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.
It is generally said that maximizing forward gain of yagi antennas sacrifices good pattern while to gain good pattern sacrifices the forward gain. The same relationships are found among SWR, gain, and pattern. Certainly, if you maximize the forward gain of your antenna, you can send the strongest signal in the forward direction. However, when you receive the weak signal you will lost the chance to make contacts with such weak signal senders because maximizing the forward gain yields the good S/N ratio over the omnidirectional noises. The same thing is applied to SWR bandwidth. You might hear the good reputation of M2 Inc's antennas. Their antennas may fairly put values on the forward gain. However, for example, if you check the SWR bandwidth of its 11 element yagi for 6m, you will find that it keeps a fairly wide SWR bandwidth.(SWR 1:1.5 --- More than 1MHz by YO, and more than 600KHz by AO)
Many persons used to try to maximize the front-to-back (F/B) ratio. Conventional way to calculate F/B ratio is measured by comparison between the forward power at 0 degrees and the power radiated in the opposite side (at 180 degrees). Maximizing the F/B ratio is certainly one of the methods to reduce the minor-lobes of the antenna. However, yagi antennas often have large back-lobes at the angles other than 180 degrees. Thus, when I designed the following antenna, I took the F/R ratio into account. Both YO and AO provide the optimizating function for the F/R ratio; that is front-to-rear ratio. According to the AO's manual, "AO provides two methods for characterizing power within the F/B region. The first method simply averages power over the region. When F/B is optimized using this method, total power within the F/B region is minimized. This is particularly useful with EME arrays to minimize total earth noise on receive. The second method uses an L4 norm. This mathematical technique is like that used to compute RMS power, but the fourth power and root are used instead of the second. F/B computed using the L4 norm is approximately the ratio of forward power to the power of the peak lobe within the F/B region. When F/B is optimized using peak power, backlobe amplitudes are minimized while maintaining roughly equal amplitude. This method is particularly useful with HF antennas since it minimizes worst-case interference from signals arriving at random rear angles".
A surrogate optimization algorithm starts with a set of points that are either randomly generated or provided as data. The optimizer evaluates the black-box model at these points and builds an initial surrogate model by fitting or interpolation.
The algorithm generates many candidate points at each iteration. The optimizer evaluates points by a merit function that typically includes the surrogate model objective value and criteria such as measures of feasibility, expected improvement, and coverage of the solution space. The optimizer can use local search or nonlinear optimization to improve the candidate set. The best candidate is evaluated with the black-box model and its value is used to update the surrogate model.
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