Antenna Design by Means of the Fruit Fly Optimization Algorithm

Polo‐López, Lucas; Córcoles, Juan; Ruiz‐Cruz, Jorge A.

doi:10.3390/electronics7010003

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…The antenna layout evolves in 136 iterations from a design with large amount of grayness and fuzzy boundaries to a final design with black and white materials as well as crisp boundaries. We stress that each pixel in this design (image) is a design variable, and that such large-scale optimization problem is computationally prohibitive to solve by stochastic optimization techniques, such as genetic algorithms [29,30]. Figure 3a shows the structure of the optimized antenna.…”

Section: Resultsmentioning

confidence: 99%

Compact Differential-Fed Planar Filtering Antennas

et al. 2019

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This paper proposes novel low-profile differential-fed planar antennas with embedded sharp frequency selectively. The antennas are compact and easy to integrate with differential devices without matching baluns. The antenna design is formulated as a topology optimization problem, where requirements on impedance bandwidth, directivity, and filtering are used as the design objectives. The optimized antennas operate over the frequency band 6.0–8.5 GHz. The antennas have reflection coefficients below −15 dB, cross-polarization levels below −42 dB, a maximum gain of 6.0 ± 0.5 dB, and a uniform directivity over more than 130° beamwidth angle in the frequency band of interest. In addition, the antennas exhibit sharp roll-off between the operational band and frequencies around the 5.8 GHz WiFi band and the 10 GHz X-band. One antenna has been fabricated with a good match between simulation and measurement results.

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Section: Resultsmentioning

confidence: 99%

Compact Differential-Fed Planar Filtering Antennas

et al. 2019

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“…As earlier, if an edge e is utilized by the net i, then x e,i = 1 else x e,i = 0. Thus, as mentioned earlier this is a BILP that is non-differentiable, and hence, cannot be solved by conventional methods such as the simplex method [8,17], the interior point method [18], etc. Some methods to solve non-differentiable optimization problems include the sub-gradient-based methods [19], the approximation method [20], etc.…”

Section: The Choice Of Decision Variablementioning

confidence: 99%

ParaLarPD: Parallel FPGA Router Using Primal-Dual Sub-Gradient Method

et al. 2019

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In the field programmable gate array (FPGA) design flow, one of the most time-consuming steps is the routing of nets. Therefore, there is a need to accelerate it. In a recent work by Hoo et al., the authors have developed a linear programming (LP)-based framework that parallelizes this routing process to achieve significant speed-ups (the resulting algorithm is termed as ParaLaR). However, this approach has certain weaknesses. Namely, the constraints violation by the solution and a standard routing metric could be improved. We address these two issues here. In this paper, we use the LP framework of ParaLaR and solve it using the primal-dual sub-gradient method that better exploits the problem properties. We also propose a better way to update the size of the step taken by this iterative algorithm. We call our algorithm as ParaLarPD. We perform experiments on a set of standard benchmarks, where we show that our algorithm outperforms not just ParaLaR but the standard existing algorithm VPR as well. We perform experiments with two different configurations. We achieve 20% average improvement in the constraints violation and the standard metric of the minimum channel width (both of which are related) when compared with ParaLaR. When compared to VPR, we get average improvements of 28% in the minimum channel width (there is no constraints violation in VPR). We obtain the same value for the total wire length as by ParaLaR, which is 49% better on an average than that obtained by VPR. This is the original metric to be minimized, for which ParaLaR was proposed. Next, we look at the third and easily measurable metric of critical path delay. On an average, ParaLarPD gives 2% larger critical path delay than ParaLaR and 3% better than VPR. We achieve maximum relative speed-ups of up to seven times when running a parallel version of our algorithm using eight threads as compared to the sequential implementation. These speed-ups are similar to those as obtained by ParaLaR.CAD (computer-aided design) tools. This can be achieved in two ways. First, by parallelizing the routing algorithms for hardware having multiple cores. However, the pathfinder algorithm [3], which is one of the most commonly used FPGA routing algorithm is intrinsically sequential. Hence, this approach seems inappropriate for parallelizing all types of FPGA routing algorithms.Second, instead of compiling the entire design together, the users can partition their design, compile partitions progressively, and then assemble all the partitions to form the entire design. Some existing works have proposed this approach [4,5]. However, the routing resources required by one partition may be held by another partition, i.e., there is no guarantee to have balanced partitions. In other words, in this approach, there is a need to tackle the difficulties arising in sharing of routing resources.The authors in ParaLaR [6] overcome the limitations of existing approaches by formulating the FPGA routing problem as an optimization problem [7]. Here, the objective function is linear an...

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“…21,22 It was developed on the behavior of fruit flies in their searching for food. Recently, the FOA has applied to handle antenna optimization problems [23][24][25] due to its advantages of faster convergence rate, shorter code, and easier to implement compared with other optimization algorithms. The procedures of the FOA method to solve an optimization problem can be presented as follows:…”

Section: Introductionmentioning

confidence: 99%

Pattern synthesis of the multimode orbital angular momentum beams based on the fruit fly optimization algorithm

Zhu

et al. 2019

Int J RF Microw Comput Aided Eng

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The multimode orbital angular momentum (OAM) radio waves can be used to multiplex multiple transmission channels to increase the capacity of communication system without adding additional bandwidth. However, the divergence of the OAM beams and beam inconsistency escalate by increasing OAM mode number. Moreover, the worse sidelobe level (SLL) always appears along with a better convergent beam. In this article, the fruit fly optimization algorithm (FOA) is proposed to suppress the SLL in multimode OAM scenario. Based on the concentric circular array antenna (CCAA), the feeding amplitudes and the radii of the array are synthesized simultaneously to realize less than −30 dB SLL of the multimode OAM patterns. When the main lobes with different OAM modes steered to a uniform azimuth of θ = 0°, the SLLs of these OAM modes are also suppressed to less than −21 dB. The advantages of FOA used in the OAM pattern synthesis are verified by comparing it with the genetic algorithm (GA). The FOA‐based synthesis has a simpler implementation flow diagram which reduces the time of synthesis to 39.5% of GA.

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Antenna Design by Means of the Fruit Fly Optimization Algorithm

Cited by 14 publications

References 22 publications

Compact Differential-Fed Planar Filtering Antennas

Compact Differential-Fed Planar Filtering Antennas

ParaLarPD: Parallel FPGA Router Using Primal-Dual Sub-Gradient Method

Pattern synthesis of the multimode orbital angular momentum beams based on the fruit fly optimization algorithm

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