Zikri Bayraktar

Fast Optimization of Electromagnetic Design Problems Using the Covariance Matrix Adaptation Evolutionary Strategy

A new method of optimization recently made popular in the evolutionary computation (EC) community is introduced and applied to several electromagnetics design problems. First, a functional overview of the covariance matrix adaptation evolutionary strategy (CMA-ES) is provided. Then, CMA-ES is critiqued alongside a conventional particle swarm optimization (PSO) algorithm via the design of a wideband stacked-patch antenna. Finally, the two algorithms are employed for the design of small to moderate size aperiodic ultrawideband antenna array layouts (up to 100 elements). The results of the two electromagnetics design problems illustrate the ability of CMA-ES to provide a robust, fast and user-friendly alternative to more conventional optimization strategies such as PSO. Moreover, the ultrawideband array designs that were created using CMA-ES are seen to exhibit performances surpassing the best examples that have been reported in recent literature.

The Wind Driven Optimization Technique and its Application in Electromagnetics

A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an

The design of miniature three-element stochastic Yagi-Uda arrays using particle swarm optimization

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Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics

-dimensional search space following Newtons second law of motion, which is also used to describe the motion of air parcels within the earths atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.

Nature-Inspired Optimization of High-Impedance Metasurfaces With Ultrasmall Interwoven Unit Cells

A fixed grid structure of reduced length is employed to generate three-element miniature stochastic Yagi-Uda arrays. Particle swarm optimization (PSO) is utilized to alter the shape and the element distances for optimum forward gain, good front-to-back ratio, and 2:1 or better voltage standing wave ratio (VSWR). Simulation results of PSO are compared with binary valued genetic algorithm (GA) optimized designs and with a conventional three-element Yagi-Uda array.

Stochastic optimization of low-loss optical negative-index metamaterial

Nature-inspired techniques such as the Genetic Algorithm (GA) [1], Ant Colony Optimization (ACO) [2], and Particle Swarm Optimization (PSO) [3] have been shown to be some of the most effective global optimization strategies. Consequently, these techniques are currently in widespread use throughout the scientific and engineering communities. In this paper, we introduce a new type of global optimization algorithm that is inspired by the motion of wind in the Earths atmosphere. We call this new nature-inspired technique Wind-Driven Optimization (WDO). WDO is a population based iterative heuristic global optimization technique for multidimensional problems. A population of infinitesimally small air parcels are distributed throughout an N-dimensional problem space and assigned random velocities such that the positions of air parcels are updated at each iteration based on the physical equations that govern large-scale atmospheric motion.

Matched Impedance Thin Planar Composite Magneto-Dielectric Metasurfaces

This letter introduces a set of novel designs for high-impedance metasurfaces with ultrasmall interwoven unit cells that achieve increased miniaturization compared to existing literature, yet still provide identical bandwidth performance and excellent field of view. This development makes possible more compact designs for artificial magnetic conducting (AMC) ground planes and electromagnetic band-gap (EBG) surfaces as well as providing the ability to scale these structures to much lower frequencies. In addition, we show that the unit cell geometry can be manipulated via wind-driven optimization (WDO) to precisely control the center frequency of the proposed high-impedance metasurface designs.

Near-infrared metamaterial films with reconfigurable transmissive/reflective properties.

Optical metamaterial consisting of metal-dielectric composites creates a complicated system that is not amenable to analytical solutions. This presents a challenge in optimizing these intricate systems. We present the application of three nature-inspired stochastic optimization techniques in conjunction with fast numerical electromagnetic solvers to yield a metamaterial that satisfies a required design criterion. In particular, three stochastic optimization tools (genetic algorithm, particle swarm optimization, and simulated annealing) are used for designing a low-loss optical negative index metamaterial. A negative refractive index around −0.8+0.2i is obtained at a wavelength of 770 nm. The particle swarm optimization algorithm is found to be the most efficient in this case.

A Real-Valued Parallel Clonal Selection Algorithm and Its Application to the Design Optimization of Multi-Layered Frequency Selective Surfaces

A novel methodology is presented for the design synthesis of matched impedance thin planar composite magneto-dielectric metasurfaces. The design synthesis involves optimizing thin, metallo-dielectric metasurfaces comprised of a periodic array of electrically small and rotationally symmetric metallic unit cells which are sandwiched between two thin dielectric layers and backed by a perfectly conducting ground plane. Optimization of the structures is carried out with a genetic algorithm (GA) to obtain a design with electromagnetic properties that are equivalent to a desired matched-impedance homogeneous medium of the same thickness. Optimized design results demonstrate the effectiveness of this new technique in synthesizing thin planar composite matched-impedance magneto-dielectric metasurfaces (MIMDM). To validate the approach, full-wave simulations of the actual metamaterial structure were compared with results obtained by employing an equivalent homogeneous effective medium and found to be in excellent agreement. Several designs are optimized with targeted applications such as substrates for miniaturized patch antennas and electromagnetic absorbing materials.

Stub-loaded inverted-F antenna synthesis via Wind Driven Optimization

A near-infrared metamaterial design that is reconfigurable between almost completely transmissive and reflective states is presented. The reconfiguration is enabled by tuning the anisotropic nematic liquid crystals used as a spacer layer between two silver nanoplates in a planar doubly periodic metamaterial. The design is optimized for maximum difference in transmittance between the two states by using a genetic algorithm. For a linearly polarized illumination at normal incidence, full-wave electromagnetic analysis predicts that the optimized metamaterial film can change the transmittance between 98.7% and 0.1% at a wavelength of 1.1 microm.