Synthesis of linear antenna array using flower pollination algorithm

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.

     

Optimization of circular antenna arrays using the cuckoo search algorithm

This article presents a study of circular antenna array design and optimization using the cuckoo search (CS) algorithm. The goal of optimization is to minimize the maximum sidelobe level with and without null steering. The CS algorithm is used to determine the parameters of the array elements that produce the desired radiation pattern. We illustrated the effectiveness of the CS in the design and optimization of circular antenna arrays by means of extensive numerical simulations. We compared our results with other methods from the literature whenever possible. We presented numerous examples that show the excellent performance and robustness of the CS algorithm and the results reveal that the design of circular antenna arrays using the CS algorithm provides acceptable enhancement compared with the uniform array or the design obtained using other optimization methods.     

基于鞅论的灰狼优化算法全局收敛性分析

灰狼优化(grey wolf optimization,GWO)算法是一种基于群体智能的随机优化算法,已成功地应用于许多复杂的优化问题的求解.尽管GWO算法有很多改进形式,但缺少严谨的收敛性分析,导致改进后的算法不具备理论支撑.对此,运用鞅论分析其收敛性.首先,根据GWO算法原理建立其基本的数学模型,通过定义灰狼状态空间及灰狼群状态空间,建立GWO算法的Markov链模型,并分析该算法的Markov性质;其次,介绍鞅理论,推导出一个上鞅作为最优适应度值的群进化序列;然后,运用上鞅收敛定理,并结合其Markov性质对GWO算法进行收敛性分析,证明GWO算法能以1的可能性达到全局收敛;最后,通过数值实验验证其收敛性能.实验结果表明,GWO算法具有全局收敛性强、计算耗时较低、寻优精度高等特点.     

Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators

This paper proposes an enhancement of the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). Then the statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). Also, the non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA. After that, the EWOA and WOA are implemented to design optimal Takagi–Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Moreover, real wind speed data has confirmed the robustness of optimal EWOA-MPPT. In conclusion, the simulation results revealed that the EWOA is a promising algorithm to be applied for solving different engineering problems.     

Wide null control of symmetric linear antenna array using novel particle swarm optimization

In this article, the method of nullifying the radiation pattern of a symmetric linear antenna array in a particular direction is propounded using an evolutionary swarm intelligence technique, Novel particle swarm optimization (NPSO). Particle swarm optimization with constriction factor approach (PSOCFA) is also adopted to compare the NPSO based results. Single or multiple wide nulls are achieved by optimum perturbations of elements current amplitude weights to have symmetric nulls about the main beam. Different numerical examples are presented to illustrate the capability of NPSO for pattern synthesis with a prescribed wide nulls locations and depths. Further, the peak Sidelobe Levels are also reduces when compared to a uniformly excited array having equal number of elements. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

一种新型非线性收敛因子的灰狼优化算法*

针对标准灰狼优化算法在求解复杂工程优化问题时存在求解精度不高和易陷入局部最优的缺点,提出一种新型灰狼优化算法用于求解无约束连续函数优化问题。该算法首先利用反向学习策略产生初始种群个体,为算法全局搜索奠定基础;受粒子群优化算法的启发,提出一种非线性递减收敛因子更新公式,其动态调整以平衡算法的全局搜索能力和局部搜索能力;为避免算法陷入局部最优,对当前最优灰狼个体进行变异操作。对10个测试函数进行仿真实验,结果表明,与标准灰狼优化算法相比,改进灰狼优化算法具有更好的求解精度和更快的收敛速度。     

Limits of compensation in a failed antenna array

In large antenna arrays, the possibility of occurrence of faults in some of the radiating elements cannot be precluded at all times. In such situations, the radiation pattern of the array gets distorted, mostly with an increase in sidelobe level and decrease in gain. Although it is not possible to restore the pattern fully by rearranging the excitations of the functioning elements, compensation methods have been reported in the literature for restoring one performance parameter of the array and making a trade‐off on some other parameter. In this article, we have made a study on the tolerance level of this compensation process. One part of the study deals with the thinning in the failed array, that is, to find a limit on the minimum number of functioning elements of the array that can restore the digital beamforming of the failed array. The second part of study deals with finding the maximum number of element failures that can be compensated. The study was carried out by optimizing the amplitude excitations of the failed array. Instead of classical optimization techniques, particle swarm optimization was used for the compensation process. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:635–645, 2014.     

An extended particle swarm optimization algorithm for pattern synthesis of conformal phased arrays

In this article, an extended particle swarm optimization (EPSO) algorithm is proposed for designing conformal phased arrays. On the basis of traditional particle swarm optimization (PSO), novel velocity updating mechanism, new exceeding boundary control operator, and global best perturbation are introduced in EPSO to overcome the drawbacks of PSO. To validate the efficiency of the proposed algorithm, both the classical test functions and the scenarios concerning a 1 × 9‐element cylindrical conformal phased array and a 3 × 9(27)‐element cylindrical conformal array with flat‐top shaped‐beam pattern are presented. Simulation results show that the proposed method is superior to genetic algorithm (GA) and PSO when applied to both the classical test functions and the practical problems of conformal antenna array synthesis. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Synthesis of thinned concentric circular antenna arrays using teaching‐learning‐based optimization

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the teaching‐learning‐based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:443–450, 2014.     

A swarm intelligence approach to the synthesis of two-dimensional IIR filters

The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a constrained minimization problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques.     

Design of multiobjective reconfigurable antenna array with discrete phase shifters using multiobjective differential evolution based on decomposition

The reconfigurable design problem is to find the element that will result in a sector pattern main beam with side lobes. The same excitation amplitudes applied to the array with zero phase should be in a high directivity, low‐side lobe pencil‐shaped main beam. This work presents a multiobjective approach to solve this problem. We consider two design objectives: the minimum value for the dual beam and the dynamic range ratio in qualify the entire array radiation pattern in order to achieve the optimal value between the antenna‐array elements. We use a recently developed and very competitive multiobjective evolutionary algorithm, called MOEA/D. This algorithm uses a decomposition approach to convert the problem of approximation of the Pareto Front into a number of single objective optimization problems. We illustrate that the best solutions obtained by the MOEA/D can outperform stat‐of‐art single objective algorithm: generalized generation‐gap model genetic algorithm (G3‐GA) and differential evolution algorithm (DE). In addition, we compare the results obtained by MOEA/D with those obtained by one of the most widely multiobjective algorithm called NSGA‐II and mutliobjective DE. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 675–681, 2012.     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

求解约束优化问题的改进灰狼优化算法

针对基本灰狼优化(GWO)算法存在求解精度低、收敛速度慢、局部搜索能力差的问题,提出一种改进灰狼优化(IGWO)算法用于求解约束优化问题。该算法采用非固定多段映射罚函数法处理约束条件,将原约束优化问题转化为无约束优化问题,然后利用IGWO算法对转换后的无约束优化问题进行求解。在IGWO算法中,引入佳点集理论生成初始种群,为算法全局搜索奠定基础;为了提高局部搜索能力和加快收敛,对当前最优灰狼个体执行Powell局部搜索。采用几个标准约束优化测试问题进行仿真实验,结果表明该算法不仅克服了基本GWO的缺点,而且性能优于差分进化和粒子群优化算法。     

Taguchi's method for multi‐objective optimization problems

Taguchi's method is a quality design technique whose applications in numerical single‐objective optimization have been recently exploited. In this article, a novel multi‐objective (MO) algorithm based on Taguchi's technique is illustrated and its performances assessed. Validation is performed through a comparison between the presented algorithm and a MO genetic algorithm (GA) based optimization, first on different sets of test functions and then on a practical antenna array synthesis problem. Results indicate a generally better behavior of the proposed algorithm in terms of convergence and spreading over the Pareto front with respect to the GA benchmark. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2013.     

Analysis of uniform circular arrays for adaptive beamforming applications using particle swarm optimization algorithm

In this article, the particle swarm optimization algorithm is used to calculate the complex excitations, amplitudes and phases, of the adaptive circular array elements. To illustrate the performance of this method for steering a signal in the desired direction and imposing nulls in the direction of interfering signals by controlling the complex excitation of each array element, two types of arrays are considered. A uniform circular array (UCA) and a planar uniform circular array (PUCA) with 16 elements of half‐wave dipoles are examined. Also, the performance of an adaptive array using 3‐bit amplitude and 4‐bit phase shifters are studied. In our analysis, the method of moments is used to estimate the response of the dipole UCAs in a mutual coupling environment. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Performance of a circular crossed‐dipole array for SDMA configuration adopting directivity and polarization control using particle swarm optimization algorithm

In this article, the performance of a circular crossed‐dipole array (CCDA) for space division multiple access (SDMA) configurations adopting directivity and polarization control is presented. The array consists of 12 dual‐polarized elements uniformly distributed in a circular configuration; each dual‐polarized element (crossed‐dipole) consists of two half‐wave dipoles in a ±45° slant configuration. The modified particle swarm optimization and moment of method (MPSO‐MOM) algorithm is used to calculate the complex weightings of the array elements in a mutual coupling environment for beamforming synthesis. In addition, the performance of the adaptive array using discrete feedings (1‐bit amplitude and 4‐bit phase shifters or only 4‐bit phase shifters) is studied. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.     

基于仿生学的优化算法及其在信号处理中的应用

仿生学优化算法是一类模仿生物行为和自然界现象的仿生算法,其目的是求解优化问题的全局最优解。本文首先介绍了各种仿生学优化算法的起源和基本原理,主要包括蚁群优化算法、粒子群优化算法、细菌觅食优化算法、蜂群优化算法、鱼群优化算法、萤火虫群优化算法、狼群优化算法、蝙蝠算法、鸡群优化算法、进化算法、免疫算法、克隆选择算法和小世界网络等。然后总结了仿生优化算法的研究现状,并给出了仿生优化算法在信号处理、图像处理、语音处理和通信网络等领域中的典型应用。最后,归纳了仿生学优化算法的特点,并对如何扩展其适用范围、探索新的仿生学优化算法提出了基本思路,对其发展进行了展望。     

Efficient design of compact millimeter wave microstrip linear array with bandwidth enhancement and sidelobe reduction

In this article, a novel linear mmWave antenna array with series‐feed network is proposed to enhance the bandwidth and reduce sidelobe level without increasing the patch size. The proposed linear array is consisted of four identical wideband array elements, which are all under operation TM₁₀ and TM₀₂ modes by loading shorting pin and rectangular slots. Additionally, through loading symmetry circle‐shaped slots for the four elements, impedance matching of linear array is achieved. Furthermore, multi‐parameters unified‐optimization (MPUO) based on imperial competition algorithm (ICA) is proposed to uniformly optimize all linear array parameters. To verify this design, the proposed linear array is fabricated with a small patch area of 7.5 × 3.914 × 0.254 mm³. The measured results show that the bandwidth is enhanced to 2.05GHz, which is 0.57GHz wider than that of simulation. The simulated peak gain reaches 13dBi while the sidelobe level is reduced to about ?19 dB at 28.6GHz. Moreover, the computation cost using MPUO is reduced by 98.12% compared with that of independent parameters optimization.     

Nonconvex/nonsmooth economic load dispatch using modified time‐varying particle swarm optimization

In this paper, a modified time‐varying particle swarm optimization (MTVPSO) is proposed for solving nonconvex economic load dispatch problems. It is a variant of the traditional particle swarm optimization (PSO) algorithm. In an MTVPSO, novel acceleration coefficients for cognitive and social components are presented as linear time‐varying parameters in the velocity update equation of the PSO algorithm. In the early stages of the optimization process, it improves the global search capability of particles and directs the global optima at the end stage. Additionally, a linearly decreased inertia weight is introduced in an MTVPSO, instead of a fixed constant value, which helps improve the diversity of the population. Through this modification mechanism in PSO, the proposed algorithm has a higher probability of avoiding local optima, and it is likely to find global optima more quickly. Six complex benchmark functions have been used to validate the effectiveness of the proposed algorithm. Furthermore, to demonstrate its efficiency, feasibility, and fastness, six different cases (3‐, 6‐, 13‐, 15‐, and 40‐unit systems and one large‐scale Korean power 140‐unit system) of the economic load dispatch problem are solved by an MTVPSO. The results of the proposed algorithm have been compared with state‐of‐the‐art algorithms. It was found that the proposed MTVPSO can deliver better results in terms of solution quality, convergence characteristics, and robustness.