Linear antenna array synthesis using cat swarm optimization

Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.     

Synthesis of linear aperiodic array using Cauchy mutated cat swarm optimization

A novel Cauchy mutated cat swarm optimization (CMCSO) that features effective global search capabilities with fast convergence is introduced in this paper. The Cauchy mutation enables the cats of the cat swarm optimization (CSO) algorithm to seek their positions in directions that avoid the problem of premature convergence and local optima. In this communication, CMCSO is applied to the synthesis of linear aperiodic arrays for minimizing sidelobe level and controlling the null positions. Various synthesis examples are considered and the obtained results are compared with linear aperiodic array designs from literature. Numerical results demonstrate that the proposed method is superior to existing methods in terms of accuracy and convergence speed. Some of the synthesized aperiodic array designs are implemented with wire dipole antenna elements using a full-wave electromagnetic simulator. Furthermore, experiments are conducted on several standard benchmark complex multimodal problems to demonstrate the effectiveness of the proposed method. The sensitivity analysis is performed on different parameters of CMCSO to demonstrate their influence on the overall performance of the benchmark and antenna array synthesis problems.     

Design of different planar geometries of antenna arrays for isoflux radiation in GEO satellites

The synthesis of different planar geometries of antenna arrays for isoflux radiation is presented in this paper. This synthesis considers the reduction of the side lobe level and the isoflux radiation requirements for Geostationary Earth Orbit satellites. The behavior of the radiation is studied in three geometries of two-dimensional antenna arrays such as uniform planar arrays, aperiodic planar arrays (APA) and concentric ring arrays (CRA). The well-known methods of genetic algorithm and particle swarm optimization are utilized for the optimization problem. In this way, the designs of APA and CRA presented in this paper could provide an acceptable solution for reducing the antenna hardware and simplifying the power feeding even more than results presented previously in the literature.     

Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution

Microwave filters play an important role in modern wireless communications. A novel method for the design of multilayer dielectric and open loop ring resonator (OLRR) filters under constraints is presented. The proposed design method is based on generalized differential evolution (GDE3), which is a multiobjective extension of differential evolution (DE). GDE3 algorithm can be applied for global optimization to any engineering problem with an arbitrary number of objective and constraint functions. GDE3 is compared against other evolutionary multiobjective algorithms like nondominated sorting genetic algorithm-II (NSGA-II), multiobjective particle swarm optimization (MOPSO) and multiobjective particle swarm optimization with fitness sharing (MOPSO-fs) for a number of microwave filter design cases. In the multilayer dielectric filter design case a predefined database of low loss dielectric materials is used. The results indicate the advantages of this approach and the applicability of this design method.      

相控阵天线的互耦和近场校准

本文在分析相控阵天线近场特性的基础上,根据信号子空间的基本理论,结合模拟退火算法,提出了一种利用近场辅助源估计相控阵天线互耦系数的方法.大量的计算机仿真结果表明,本方法使用校准源少,校准源到天线阵距离近,算法稳健,即使在雷达工作、并且存在其它未知信号时,也能准确估计出天线阵的互耦系数.     

Comparison of different antenna arrays for the BER reduction in indoor wireless communication

In this paper, we use the ultra wideband antenna array combined with particle swarm optimization (PSO) to minimize the BER for indoor communication systems. Three types of antenna arrays, namely the circular shape, L shape, and Y shape arrays, are used in the transmitter and their corresponding BER on several paths in the indoor environment are calculated. On the basis of the topography of the antenna array and the BER formula, the array pattern synthesis problem can be reformulated into an optimization problem and solved by the PSO. The novelties of our approach is not only choosing BER as the object function instead of the sidelobe level of the antenna pattern, but also considering the antenna feed length effect of each array element. The particle swarm optimization algorithm is employed to optimize the excitation voltages and feed lengths for these antenna arrays to reduce the BER. The strong point of the PSO is that it can find the solution even if the performance index cannot be formulated by simple equations. By the obtained antenna patterns, we can know the route with the lowest BER; meanwhile, transmission power using this route can be reduced. Numerical results show that the synthesized antenna array pattern is effective in focusing maximum gain to the line‐of‐sight path for these antenna arrays. The synthesized array pattern also can mitigate severe multipath fading in complex propagation environments. As a result, the BER can be reduced substantially in indoor ultra wideband communication systems. The investigated results can help communication engineers improve their planning and design of indoor wireless communication.Copyright © 2011 John Wiley & Sons, Ltd.     

Array Pattern Optimization for Steerable Circular Isotropic Antenna Array Using Cat Swarm Optimization Algorithm

In this paper the steerable isotropic circular array antenna is designed for reducing the side lobe level (SLL) using evolutionary optimization technique. The optimization techniques particle swarm optimization and cat swarm optimization algorithm are used to reduce the SLL as well as to steer the main beam in specific direction. In this design of steerable circular arrays the amplitude excitations are optimized. Obtained results show that the maximum peak of SLL of the resultant patterns are as per requirement. This paper present a good performance in the array factor response and suppressed SLL for different number of array elements with different steering angle of the main beam using evolutionary optimization technique.     

Particle swarm optimization versus genetic algorithms for phased array synthesis

Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far-field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e., particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. The particle swarm optimizer shares the ability of the genetic algorithm to handle arbitrary nonlinear cost functions, but with a much simpler implementation it clearly demonstrates good possibilities for widespread use in electromagnetic optimization.     

粒子群阵列天线方向图适应值函数研究

在粒子群算法优化阵列天线方向图时,适应值函数的选取对算法收敛和优化效率都有着至关重要的影响。针对复杂多指标方向图优化容易早熟收敛,提出了一种分步的适应值函数策略,通过分阶段提高优化指标,可以更好地促进算法收敛和提升优化效率。仿真结果表明:将此适应值策略应用于天线方向图综合中,在不改变算法本身的同时,可以在多零点和低旁瓣约束情况下取得更好的优化效果。     

Synthesis of UWB planar antennas by means of natural optimization algorithms

Results of synthesis of planar dipole and modified Vivaldi antenna that provide specified reflection coefficient and directivity within frequency band 3.1–10.6 GHz are presented. The required values of the parameters are achieved through directed modification of arms’ profiles of the dipole antenna and a slot profile of the Vivaldi antenna. Synthesis procedure is performed by means of three different natural algorithms of global optimum search, namely by genetic algorithm, particle swarm optimization algorithm and a novel neuron-genetic algorithm suggested by the authors. The last one is built upon artificial neuron networks and their inversion by genetic algorithm. Synthesis results are confirmed by experimental data.     

应用于负荷经济分配的改进差分进化算法

为了求解电力系统负荷经济分配问题,提出一种改进差分进化算法.该算法考虑机组的爬坡约束、出力限制区约束等非光滑费用函数曲线等非线性特性,采用词典排序法处理系统约束来保证算法结果严格满足约束条件,保证了系统的稳定性和安全性.在差分进化算法的交叉算子计算中引入微粒群算法中的个体最优和全局最优的概念,并采用遗传微粒群算法的多点交叉机制,将两者以一定的比率引入试验向量增强算法的局部搜索能力.此算法被应用于一个6台机组的算例,与遗传算法、微粒群算法和标准差分进化算法相比较,改进的差分进化算法的结果质量更好并且更稳定,是求解负荷经济分配问题的一种有效方法.     

多策略差分进化的元胞多目标粒子群算法

为了增加Pareto解集的多样性,平衡多目标优化的全局搜索和局部寻优的能力,提出一种多策略差分进化的元胞多目标粒子群算法.该算法在分析粒子群优化原理基础上,将元胞自动机理论融入粒子群算法,研究粒子种群的交流结构和信息传递机制.为了避免粒子飞行速度过快陷入局部收敛,提出一种限制粒子飞行速度的策略,并引入一种多策略差分进化选择算子增加对粒子的扰动.实验证明,该算法相对于比较算法,有更好的收敛性和多样性.     

基于改进粒子群算法的天线方向图综合技术

针对基本粒子群算法的早熟收敛、易收敛于局部极值的特点，提出一种改进的粒子群优化算法，采用对全局最佳微扰和惯性权重跳变阈值的设置改善了算法的优化速度和收敛精度。经过对一系列测试函数的计算，证明该方法具有良好的优化效果。最后，给出了该方法应用于阵列天线方向图综合中的模型和仿真实例。     

基于混沌粒子群算法的宽零陷方向图综合方法

自适应阵列天线常需要采用宽零陷技术,以增强阵列天线抗干扰的稳健性。为此,提出了一种基于混沌粒子群算法（CPSO）的阵列天线宽零陷方向图综合方法。该算法首先采用混沌序列初始化粒子位置,以增强搜索多样性,并在对部分非优胜粒子的位置更新时引入混沌扰动项,在每次迭代中对全局最优位置进行变尺度混沌优化,提高了全局和局部搜索能力,加快了收敛速度。仿真结果验证了混沌粒子群算法在阵列天线宽零陷方向图综合时的收敛速度和精度方面均优于标准粒子群算法。     

New optimisation algorithm for planar antenna array synthesis

Deterministic techniques have been extensively employed in antenna array synthesis, using physical parameters of the array to determine an optimal solution. These deterministic algorithms, requiring good initial conditions, can become unstable and require long convergence times. In contrast, optimisation methods using stochastic principles have become popular techniques for solving the synthesis problem. The cross entropy (CE) method is one of those more popular algorithms, such as genetic algorithm (GA) and particle swarm optimisation (PSO), possessing good convergence properties. Then, the CE method is a global stochastic optimisation technique for solving both continuous and discrete systems.This paper describes the planar antenna arrays synthesis using the CE method to produce array responses with minimum peak sidelobe levels (SLL).Good simulation results are obtained with SLL of ?22.7 dB for isotropic elements and ?32.17 dB for non-isotropic elements. By comparison to more popular algorithms, the CE method can optimise a large number of discrete parameters in a short time but GA requires a long time (large iteration number) to achieve the same performance.     

Synthesis of antenna arrays using genetic algorithms

In this article, two particular methods for the synthesis of the complex radiation pattern of a linear and a planar array, based on genetic algorithms (GAs), are discussed. First, we present a combination of Schelkunoff's method and GAs for linear arrays with arbitrary radiation patterns; then, we consider the synthesis of planar arrays with rectangular cells. The results show the flexibility of GAs to solve complex problems related to antenna synthesis, subject to many restrictions which are difficult to treat by classical optimization techniques. There is good agreement between the desired and calculated radiation patterns     

基于粒子群算法的阵列波束宽零陷优化设计

作为一种新型的群智能随机优化算法,粒子群优化（PSO ）算法在解决各种非线性复杂问题上得到了广泛的应用。研究了粒子群优化算法在阵列天线方向图零陷加宽技术上的应用,通过实例表明粒子群算法能够在较宽的角度区间上形成满足要求的零陷,在抗干扰应用方面具有较高的应用前景。     

求解多输入多输出检测新算法——遗传粒子群优化

设计两种基于粒子群优化算法(PSO)和基于遗传算法(GA)的多输入多输出(MIMO)系统检测算法.提出一种新的融合GA和PSO进化机制的遗传粒子群进化(GPSO)算法,并将其应用于MIMO系统检测问题求解.新算法改善了初始化种群,并将每一代粒子划为精英粒子、次优粒子和糟糕粒子三部分,对这三种粒子分别采用极值扰动、PSO...     

基于进化计算的二元序列优化算法研究

具有良好非周期自相关特性二元序列在通信同步、雷达等领域具有广泛的应用。通过对遗传算法、粒子群算法与量子粒子群算法三种进化算法进行对比分析,设计了具有良好非周期自相关特性的二元序列的搜索算法。研究结果表明,粒子群算法的搜索能力优于遗传算法,而量子粒子群算法具有参数少,易于控制的优点,取得了较好的优化结果。     

Comparison of adaptive-network-based fuzzy inference system models for resonant frequency computation of circular microstrip antennas

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared to the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm. Published in Russian in Radiotekhnika i Elektronika, 2009, Vol. 54, No. 4, pp. 389–400. The article is published in the original.