Antenna Optimization With a Computationally Efficient Multiobjective Evolutionary Algorithm

Abstract

designed antenna consists of three pairs of split-ring loops and a tapered transmission line. The simulated and measured results show that the proposed UWB antenna has a wide bandwidth from 2 to 20 GHz, and all the measured return losses are less than 010 dB in this band.The graph of the magnitude of the transfer function is relatively smooth combined with a flat group delay in the measured band. The simple planar geometry also makes it compatible with the existing microwave integrated circuit. REFERENCES Antenna Optimization With a Computationally Efficient Multiobjective Evolutionary AlgorithmMatthias John and Max J. AmmannAbstract-An efficient multiobjective evolutionary algorithm is described for optimizing a novel spline based printed monopole antenna. The antenna geometry is based on spline outlines. Both radiating element and groundplane are simultaneously optimized by the algorithm. The resulting antenna performance is evaluated. It is shown that the evolutionary algorithm and the spline geometry can be used to efficiently generate ultrawideband antennas on limited computing resources.Index Terms-Antenna optimization, multiobjective evolutionary algorithm, ultrawideband (UWB) antenna, spline.

RF breast cancer detection employing a noncharacterized vivaldi antenna and a MUSIC-inspired algorithm

“…The three tumors are located in (20,20) mm, (−25, 25) mm and (−5, −10) mm, respectively. First, we consider the same configuration with 12 scanning positions as above.…”

“…In [18] Vivaldi antennas are miniaturized by corrugating the external flanges to operate as breast cancer sensors in the frequency range 3.1 − 6.85 GHz. Efficient Global Optimization (EGO) techniques can also be employed in order to improve their UWB features [19], [20]. EGO optimisation was used in [13], where a planar antipodal Vivaldi antenna is evaluated in terms of operating bandwidth, fidelity factor and field penetration into a planar breast phantom.…”

Performance evaluation of antennas for UWB radar and positioning systems

“…Two different optimisation algorithms have been used to arrive at a final geometry for the antennas. Antenna B was optimised by a Genetic Algorithm [6] and antenna C by a more efficient Evolutionary Global Optimisation algorithm [7]. These algorithms take the coordinates of the control points as parameters and evolve them towards a performance goal.…”

Recent developments in simulation-driven multi-objective design of antennas

“…However, real-world antenna design tasks are multi-objective ones. In particular, if the designer priorities are not clearly defined beforehand, identifying a set of alternative design representing the best possible trade-offs between conflicting objectives may be of fundamental importance (e.g., in order to determine limitations of a given antenna structure and its suitability for a given application) [16][17][18][19]. Nowadays, population-based metaheuristics are undoubtedly the most popular solution approaches for handling multi-objective antenna design problems.…”

“…Nowadays, population-based metaheuristics are undoubtedly the most popular solution approaches for handling multi-objective antenna design problems. Techniques such as multi-objective genetic algorithms (GAs) and particle swarm optimizers (PSO), e.g., [16,[18][19][20][21][22][23], allow finding the entire Pareto front in one algorithm run. However, their disadvantage is high computational cost (hundreds, thousands or even tens of thousands of objective function evaluations), which becomes a serious bottleneck if high-fidelity discrete EM simulations are involved in antenna evaluation process.…”