Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review
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paper_title: Application of Genetic Algorithm to the Optimization of Gain of Magnetized Ferrite Microstrip Antenna

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The application of Genetic Algorithm (GA) to the optimization of gain of microstrip antenna, fabricated on ferrite substrate, biased externally by a steady magnetic field, is reported. The fitness function for the GA program is developed using cavity method for the analysis of microstrip antenna. The computed results are compared with the results obtained using GA optimizer of MATLAB.
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paper_title: Nonuniform Overlapping Method in Designing Microstrip Patch Antennas Using Genetic Algorithm Optimization

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Genetic algorithm (GA) has been a popular optimization technique used for performance improvement of microstrip patch antennas (MPAs). When using GA, the patch geometry is optimized by dividing the patch area into small rectangular cells. This has an inherent problem of adjacent cells being connected to each other with infinitesimal connections, which may not be achievable in practice due to fabrication tolerances in chemical etching. As a solution, this paper presents a novel method of dividing the patch area into cells with nonuniform overlaps. The optimized design, which is obtained by using fixed overlap sizes, shows a quad-band performance covering GSM1800, GSM1900, LTE2300, and Bluetooth bands. In contrast, use of nonuniform overlap sizes leads to obtaining a pentaband design covering GSM1800, GSM1900, UMTS, LTE2300, and Bluetooth bandswith fractional bands with of 38% due to the extra design flexibility.
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paper_title: A HIGH-DIRECTIVITY MICROSTRIP PATCH ANTENNA DESIGN BY USING GENETIC ALGORITHM OPTIMIZATION

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A high-directivity patch antenna with broadside directiv- ity is attractive, since a narrow beam can be obtained without the need of using an array of antennas. Therefore, the solution becomes simpler as there is no need for a complicated feeding network. In this sense, this paper presents a novel patch antenna design with high directiv- ity in the broadside direction by using genetic algorithms (GA). The proposed GA method divides the overall patch area into difierent cells taking into account that cells have a small overlap area between them. This avoids optimized geometries where cells have only an inflnitesimal connection. Therefore, the proposed method is robust for manufactur- ing. The antenna operates in a higher-order mode at 4.12GHz and the geometry flts inside a patch of 40mm £ 40mm on a substrate with a relative permittivity of 3.38 and a thickness of 1.52mm resulting in a directivity of 10.5dBi. The specialty of this design is the use of GA to select the optimized shape and the feeding position instead of a known shape and a flxed feeding position. The antenna has been fabricated and the simulation results are in good agreement with the measure- ments. This results in a simpler design of a single high-directivity patch, which can substitute an array of two elements operating in the fundamental mode.
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paper_title: Miniaturized biocompatible microstrip antenna using genetic algorithm

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Biocompatible antennas are an area of recent research that can facilitate remote communication with medical implants. This paper shows several possible designs of a "waffle-type" antenna created using genetic algorithms (GAs) that are of a size potentially suitable for cardiac pacemakers in the 402-405 MHz MICS band. In addition, methods to constrain the simulation and speed up the convergence of the GA for this type of antenna are explored. Even simple constraints such as fixing the feed and ground locations and encouraging the antenna to grow preferentially in the horizontal direction to take advantage of the longer physical dimensions of a pacemaker in that direction can appreciably improve convergence speed. One exception to this was constraining the patches to be connected together rather than distributed randomly, which caused the system to converge slower rather than faster. Also notable was that when a larger physical size was allowed, the system also converged more quickly despite a sizeable increase in the number of unknowns (subpatches) in the model. Thus, this paper provides a smaller, better matched microstrip antenna for biotelemetry and a choice of GA constraints for designing it.
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paper_title: A novel miniature multi-frequency broadband patch antenna for WLAN applications

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Miniature antennas with multi frequency and broadband features have a high demand in the field of wireless communications. This paper proposes such a patch antenna for WLAN applications in UNII-1, UNII-2, UNII-2 extended and UNII-3 bands. The specialty of the antenna is its compact size with a foot print of only 8 mm × 4 mm. The patch with a shorting pin is etched on a substrate with a dielectric constant of 3.2 and a thickness of 0.762 mm and is suspended in air 5 mm above a ground plane. The patch geometry, feed point and shorting pin positions are optimized using genetic algorithms. The designed antenna displays a -10 dB fractional impedance bandwidth of 12.6% and is suitable for handheld devices.
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paper_title: Genetically engineered multiband fractal antennas

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Fractal antenna engineering concepts have been successfully combined with genetic algorithms to develop a powerful design optimisation tool. The genetic optimisation approach developed can simultaneously optimise the geometry of a fractal antenna, locations of loads, component values of loads, and projected length of the fractal antenna. The results suggest that a 30 to 55% size reduction can be achieved by optimising the fractalisation of a given antenna. The knowledge gained from this study is directly applicable to the design of miniature multiband fractal antennas.
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paper_title: Joint Optimization of Microstrip Patch Antennas Using Particle Swarm Optimization for UWB Systems

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Ultra wideband (UWB) systems are the most appropriate for high data rate wireless transmission with low power consumption. However, the antenna design for UWB has been a challenging task. Moreover, it is always desirable to have more freedom by designing different shape antennas with identical characteristics so that they can be used in either transmitter or receiver depending on other physical constraints such as area. To tackle these issues, in this paper, we have investigated a joint optimization of three different shape-printed monopole antennas, namely, printed square monopole antenna, printed circular monopole antenna and printed hexagonal monopole antenna, for UWB applications. More specifically, we have obtained the optimized geometrical parameters of these antennas by minimizing the mean-square-error for desired lower band edge frequency, quality factor, and bandwidth. The objective of joint optimization is to have identical frequency characteristics for the aforementioned three types of PMA which will give a freedom to interchangeably use them at either side, transmitting or receiving. Moreover, we employ particle swarm optimization (PSO) algorithm for our problem as it is well known in the literature that PSO performs well in electromagnetic and antenna applications. Simulation results are presented to show the performance of the proposed design.
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paper_title: Nature-Inspired Optimization Techniques in Communication Antenna Designs

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This paper summarizes the primary features inherent in current optimization methods typically applied to antenna designs and demonstrates their effectiveness by applying particle swarm optimization (PSO), a nature-inspired global optimization technique, to novel antenna design solutions in wireless communications. The concept of the PSO technique is briefly introduced and an outline of the important parameters that are utilized is summarized. Next, an implementation strategy combining PSO with numerical algorithms for electromagnetic solutions, namely the finite element method (FEM) and the method of moments (MoM), is discussed. In both realizations (PSO-FEM and PSO-MoM), the PSO technique drives the design variables, such as the antenna dimensions, geometrical features, etc., and the full-wave electromagnetic analysis engines evaluate the fitness function for the optimizer. Optimized antenna designs including a multiband handset antenna and an E-shaped patch antenna for circularly polarized (CP) applications are presented. Measurement results of prototype optimized designs are shown to demonstrate the functionality and effectiveness of the methodologies presented in this paper.
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paper_title: Design of a Novel UWB Omnidirectional Antenna Using Particle Swarm Optimization

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A UWB E-plane omnidirectional microwave antenna is designed and fabricated for IEEE 802.11a communication system and microwave magnetron source system as a radiation monitor. A cooptimization method based on particle swarm optimization (PSO) algorithm and FDTD software is presented. The presented PSO algorithm is useful in many industrial microwave applications, such as microwave magnetron design and other techniques with a high power level. The maximum measured relative bandwidth of 65% is achieved for the proposed antenna after a rapid and efficient optimization. Furthermore, the measured antenna polarization purity reaches about 20 dB at the communication C band. The PSO algorithm is a powerful candidate for microwave passive component design.
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paper_title: Parallel particle swarm optimization and finite- difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs

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This paper presents a novel evolutionary optimization methodology for multiband and wide-band patch antenna designs. The particle swarm optimization (PSO) and the finite-difference time-domain (FDTD) are combined to achieve the optimum antenna satisfying a certain design criterion. The antenna geometric parameters are extracted to be optimized by PSO, and a fitness function is evaluated by FDTD simulations to represent the performance of each candidate design. The optimization process is implemented on parallel clusters to reduce the computational time introduced by full-wave analysis. Two examples are investigated in the paper: first, the design of rectangular patch antennas is presented as a test of the parallel PSO/FDTD algorithm. The optimizer is then applied to design E-shaped patch antennas. It is observed that by using different fitness functions, both dual-frequency and wide-band antennas with desired performance are obtained by the optimization. The optimized E-shaped patch antennas are analyzed, fabricated, and measured to validate the robustness of the algorithm. The measured less than - 18 dB return loss (for dual-frequency antenna) and 30.5% bandwidth (for wide-band antenna) exhibit the prospect of the parallel PSO/FDTD algorithm in practical patch antenna designs.
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paper_title: Optimization of microstrip patch antenna using Particle swarm optimization with curve fitting

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An optimization technique for microstrip patch antenna using particle swarm optimization (PSO) with curve fitting is introduced in this paper. An inverted E-shaped microstrip patch antenna designed for IMT-2000 band is utilized to demonstrate the optimization technique. The data for curve fitting is obtained from Zealand IE3D software by varying different geometrical parameters of the antenna. Using the data, the equations representing the relationship among different parameters of a microstrip antenna are generated. Graphmatica curve fitting software was used to generate the curve. The PSO program was developed and executed in MATLAB and finally the optimized antenna was simulated in Zealand IE3D. Comparison between conventional antenna and curve fitting based PSO optimized antenna showed remarkable improvement over bandwidth. For the inverted E-shaped microstrip patch antenna, the bandwidth increased by 15%.
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paper_title: Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations

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The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems
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paper_title: Wideband E-shaped patch antenna design for wireless communications using self-adaptive differential evolution

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Wideband E-shaped patch antenna design is presented. The design technique is based on a self-adaptive Differential Evolution (DE) algorithm. In order to evaluate the algorithm's performance we compare results with three other common evolutionary algorithms regarding statistical results and convergence speed. The results show that the self-adaptive DE algorithm outerperforms other global optimizers like Particle swarm optimization (PSO) variants and the DE rand/1/bin strategy. Therefore, the results indicate the advantages of this approach and the applicability of this design method to other optimization problems in electromagnetics.
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paper_title: A 2.45GHz microstrip patch antenna evolved for WiFi application

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This paper evolves a 2.4GHz microstrip antenna for WiFi application by using the differential evolution (DE). The antenna structure is square substrate with a size of 50mm×50mm×1.6mm. The bottom side of the substrate is printed with a microstrip feed line. The top is metallic painted where a ellipse cut off. The structure is parameterized as a solution vector, and the requirements of the antenna are modeled as objective and constraints. Then a constrained optimization problem (COP) is formulated. The DE algorithm finds an antenna which satisfy the requirements in the simulating. The measured performance of the fabricated antenna matches the simulated one on the whole. This evolved antenna can be applied as a antenna for router and personal computer in indoor use. Notably, an exceptional achievement is the bandwidth of the antenna reaches over 1GHz.
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paper_title: Design of Short-Circuited Microstrip Antenna Using Differential Evolution Algorithm

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Differential evolution (DE) algorithm is used to design a microstrip antenna, loaded with a shorting pin. The position of probe and the position of shorting pin are optimized using DE. The fitness function for DE is obtained using multiport network modelling technique. Antenna is fabricated and measured results are compared with the theoretical results.
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paper_title: Performance comparison of Differential Evolution, Genetic Algorithm and Particle Swarm Optimization in impedance matching of aperture coupled microstrip antennas

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Microstrip antennas are most widely used nowadays for microwave applications because of their numerous advantages like small size and weight, conformability, compatibility with microwave integrated circuits etc. The aperture coupled microstrip line feeding technique has many advantages like availability of a large number of adjustable parameters in the form of aperture length, width and stub parameters. Design of aperture coupled microstrip antennas using a recently developed very fast optimization algorithm namely Differential Evolution [1] is proposed in this paper. The optimization algorithm is used to determine the dimensions as well as the transmission line feed positions for optimum matching over a given range of frequencies and substrate. For use of Differential Evolution in the optimal design of aperture coupled microstrip antennas, the fitness function is evaluated using the Method of Moments technique implemented through IE3D. In addition optimal design of the antenna using other conventional optimization techniques like real coded Genetic Algorithm and Particle Swarm Optimization are done and the results and performances are compared with those obtained by Differential Evolution (DE).
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paper_title: Performance Comparison of Differential Evolution, Particle Swarm Optimization and Genetic Algorithm in the Design of Circularly Polarized Microstrip Antennas

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Performance evaluation of evolutionary optimization techniques is done with respect to the design of circularly polarized microstrip antennas. The optimization techniques include conventional differential evolution, particle swarm optimization and genetic algorithm along with their advanced schemes. The optimization is done for impedance matching over a predefined frequency range, circular polarization, and high gain at a center frequency. The control parameters of all the algorithms are tuned using meta-level particle swarm optimization algorithm. Three cases are considered with three conventional feeding techniques, namely probe feed, transmission line feed, and the cross-aperture coupled feed. A fuzzified cost function is developed for optimization to ensure proper outcome from each optimization algorithm. The antenna parameters obtained from the algorithm producing best results are fabricated and their performances measured.
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paper_title: Bandwidth Enhancement of Microstrip Patch Antenna Using Jerusalem Cross-Shaped Frequency Selective Surfaces by Invasive Weed Optimization Approach

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In this paper, we present a novel approach for improving the bandwidth of a microstrip patch antenna using Jerusalem cross- shaped frequency selective surfaces (JC-FSSs) as an artiflcial magnetic ground plane. The invasive weed optimization (IWO) algorithm is employed to derive optimal dimensions of the patch antenna and JC-FSS element in order for the whole structure to work at 5.8GHz with consideration of gain. For the most e-cient design, the antenna and FSS ground plane are optimized together, rather than as separate components. Simulation results demonstrate that this optimum conflguration (the microstrip patch antenna over the artiflcial magnetic ground plane) have a broad bandwidth of about 10.44%. This wide bandwidth is obtained while the thickness of the whole structure is limited to 0.1‚. Further more desirable radiation characteristics have been successfully realized for this structure. The radiation e-ciency of the AMC antenna conflguration was found to be greater than 85% over the entire bandwidth. In general by introducing this novel Jerusalem cross artiflcial magnetic conductor (JC-AMC) in lieu of the conventional perfect electric conductor (PEC) ground plane, the bandwidth enhancement of about 67% and a thinner and lighter weight design has been obtained. Sample antenna and EBG layer are also fabricated and tested, to verify the designs. It is shown that the simulation data in general agree with the measurement results for the patch antennas implemented with FSS ground plane.
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paper_title: Stub-loaded inverted-F antenna synthesis via Wind Driven Optimization

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A novel technique to design and optimize a low profile wire antenna with high broadside gain is introduced. The desired antenna properties are achieved by loading an electrically long inverted-F antenna (IFA) with two optimal wire stubs along the body of the antenna. The best lengths and locations of the stubs as well as other antenna design parameters were selected by a new nature-inspired technique called Wind Driven Optimization (WDO). The results of this work demonstrate that an optimized stub-loaded inverted-F antenna (SLIFA) can provide not only lower profile than that of a conventional IFA but also an enhanced gain of 8.2 dB at broadside where the IFA has a null.
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paper_title: Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics

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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 Earth's 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.
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paper_title: Dual band microstrip antenna with non regular polygonal patch for satellite applications

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In this paper, a new method for designing dual band microstrip antenna is presented. This method can be used to design any dual band microstrip patch antenna with arbitrary resonant frequencies and different frequency ratios. In this method, non-regular polygonal patch is used to design dual band microstrip antenna and particle swarm optimization (PSO) technique and ant colony optimization (ACO) are used to optimize the shape of the patch antenna. This method is used to design a dual band microstrip antenna for satellite application, which has resonant frequencies at 8.6 and 13GHz.
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paper_title: An improved ant colony optimization algorithm and its application to electromagnetic devices designs

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Based on the success in the design of a new global search procedure on the development of a novel trail updating mechanism and the introduction of an elitist strategy to available ant colony optimization (ACO) methods, an improved ACO algorithm is proposed. In order to facilitate the implementation of the search procedure, the available local search phase is simplified also. The algorithm is tested on a mathematical function and an inverse problem, and its performances are compared with those of other well designed methods.
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paper_title: Bacterial foraging optimization technique to calculate resonant frequency of rectangular microstrip antenna

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Resonant frequency of rectangular microstrip antenna having various substrate thicknesses are generally calculated using the standard expressions presented in literatures. But these equations suffer from errors when compared with the experimental values due to Quasi-TEM propagation in the microstrip structure and fringing field. A number of researchers used soft computing approach such as neural networks and genetic algorithm on those equations to minimize the error for better accuracy. Bacterial foraging, an evolutionary optimization technique conceived in recent times, has many advantages over genetic algorithm and is yet to be applied on the design of microstrip patch antenna. In this article, a novel technique is developed to apply bacterial foraging optimization technique in conjunction with the expressions developed to calculate accurately the resonant frequency of rectangular microstrip antenna of any dimension and of any substrate thickness. Further, bacterial foraging is applied to the calculatation of the feed point of microstrip antenna. The technique developed in this article can be a generalized soft computing tool to calculate resonant frequency of rectangular microstrip antenna. Similarly, the idea of the article can be used for calculating the various parameters of microstrip antennas of different structure and geometry. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.
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paper_title: Parameter calculation of rectangular microstrip antenna using Biogeography-Based Optimization

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Miniaturized microstrip antennas are of great interest in the recent time especially in wireless communication systems and biomedical applications. To enhance efficiency, avoid co-channel, adjacent channel interference and associated complications accurate determination of resonant frequency is an essential requirement. A method to calculate accurately the resonant frequency is of great importance especially on such antennas like microstrip patch antennas that have inherently narrow bandwidths. The feed point calculation is also difficult in this type of antenna. Biogeography-Based Optimization (BBO) is a new bio-inspired optimization and is yet to be applied in antenna and electromagnetic engineering. In this paper, the authors have used BBO for accurate determination of resonant frequency and feed point calculation of rectangular microstrip antenna (MSA). The results are in good agreement thus encouraging the use of BBO for antenna design.
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paper_title: Simple Formulas for Calculating Resonant Frequencies of C and H Shaped Compact Microstrip Antennas Obtained by Using Artificial Bee Colony Algorithm

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Accurate and simple formulas are presented for effective length in determining the resonant frequency of C and H shaped compact microstrip antennas (CMAs) operating on UHF band. For this purpose, 144 C-shaped CMAs and 216 H-shaped CMAs having different physical dimensions and various relative dielectric constants were simulated by packaged software called XFDTD based on finite difference time domain (FDTD) method. The physical and electrical parameters of the antennas and their respective resonant frequency values were given as inputs to artificial bee colony (ABC) algorithm to determine unknown coefficients of the effective length expression which provides the best fit to the resonant frequency. The resonant frequency results of the effective length expressions were compared with the different measurement, simulation and calculation results published earlier in the literature. It was shown that our results are in very good agreement with the simulated and measured results as compared to those of calculat...
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paper_title: Optimization of the Return Loss of Differentially Fed Microstrip Patch Antenna Using ANN and Firefly Algorithm

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The microstrip patch antenna that have more than two feed points or lines is known as differential fed microstrip patch antenna. In this paper, firefly algorithm (FA) and artificial neural network (ANN) has been applied to a `Flower' shaped differentially fed microstrip patch antenna for optimizing the return loss. This new optimization method is much faster than conventional optimization methods. FA is the new nature-inspired algorithm which is based on the flashing behavior of fireflies in the summer sky in the hot and humid regions. To validate the ability of FA, the results obtained from FA are compared with that obtained using genetic algorithm (GA) and ANN, and it has been observed that FA performs better as compared to GA.
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paper_title: Comparative study of Cuckoo search optimization and differential evolution algorithm for design of microstrip fed microstrip patch antenna

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Microstrip line feed Microstrip Antenna (MSA) geometrical design parameters are Patch Width, Inset Feed Position and Patch length. Cuckoo Search algorithm (CS) is a recent optimization algorithm creating its application in the electromagnetic community for evolving the design parameters of microstrip antenna. Cuckoo Search algorithm (CS) has been employed in this paper to calculate the design parameters of the microstrip line feed microstrip antenna for a reaspective target frequencies and return loss. Same investigation is made for different microwave frequencies (1–18 GHz) and at dielectric constant 2.4. Simulations have been done using IE3D software. The results suggests Cuckoo search algorithm outperforms Differential Evolution (DE)in terms of quality of solution and convergence speed.
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Title: Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review

Section 1: Introduction
Description 1: Briefly describe classical microstrip patch antennas (MPAs), their uses, and the limitations that have driven research efforts to improve them.

Section 2: Genetic Algorithms
Description 2: Discuss the application of Genetic Algorithms (GA) in the design and optimization of MPAs, describing how GA has been used to improve various performance parameters.

Section 3: Particle Swarm Optimization
Description 3: Explain the use of Particle Swarm Optimization (PSO) in MPA design, highlighting its simplicity and effectiveness compared to other algorithms.

Section 4: Differential Evolution
Description 4: Describe the Differential Evolution (DE) algorithm and its applications in optimizing MPA parameters to achieve desired performance characteristics.

Section 5: Invasive Weed Optimization
Description 5: Provide an overview of Invasive Weed Optimization (IWO) and its recent use in designing MPAs, focusing on its effectiveness in achieving specific operational goals.

Section 6: Wind Driven Optimization
Description 6: Introduce the Wind Driven Optimization (WDO) algorithm and explore its novel usage in MPA design, emphasizing both its strengths and limitations.

Section 7: Ant Colony Optimization
Description 7: Detail the Ant Colony Optimization (ACO) algorithm and its potential applications in MPA design, despite its limited popularity in this field.

Section 8: Other Bio-Inspired Algorithms
Description 8: Summarize the application of other emerging bio-inspired algorithms such as bacterial foraging, biogeography-based optimization, artificial bee colony, and others in the context of MPA design.

Section 9: Discussion
Description 9: Analyze the overall trends and findings of applying bio-inspired algorithms to MPAs, discussing the comparative advantages and popularity of different algorithms.

Section 10: Conclusion
Description 10: Conclude the review by summarizing the impact and future potential of bio-inspired algorithms in the design and optimization of MPAs.