基于Harris Hawks优化算法的介质波导滤波器优化设计

Harris Hawks优化(Harris Hawks optimization, HHO)算法是一种模拟鸟群合作捕食行为的新型群智能算法. 介质波导滤波器是当前5G移动通信设备急需的器件,因此如何利用新型优化算法高效且精确地对介质波导滤波器进行优化设计十分重要. 文中首先描述了HHO算法流程,并结合滤波器优化问题提出了一种通用框架;然后基于稳态假设对HHO算法的更新方程进行了理论分析,依据所导出的方程分析了算法的动态特性及收敛行为;最后利用HHO算法实现了两款介质波导滤波器的优化设计. 为验证算法性能,将本文算法与三个著名的群智能算法进行比较. 实验结果表明,HHO算法的收敛速度、效率和精度都明显优于目前业内主流应用的自适应差分进化算法、花粉授粉优化算法和灰狼优化算法.     

IIR数字滤波器设计的粒子群优化算法

本文探讨了粒子群优化算法及其性能评估准则，然后重点研究了IIR数字滤波器设计的粒子群优化算法及其实现步骤。最后，通过IIR数字低通、带通滤波器设计两个实例证明了本文算法的有效性。     

The Application of Particle Swarm Optimization to Passive and Hybrid Active Power Filter Design

This paper proposes an optimal design method for passive power filters (PPFs) and hybrid active power filters (HAPFs) set at high voltage levels to satisfy the requirements of harmonic filtering and reactive power compensation. Multiobjective optimization models for PPF and HAPF were constructed. Detuning effects and faults were also considered by constructing constraints during the optimal process, which improved the reliability and practicability of the designed filters. An effective strategy was adopted to solve the multiobjective optimization problems for the designs of PPF and HAPF. Furthermore, the particle swarm optimization algorithm was developed for searching an optimal solution of planning of filters. An application of the method to an industrial case involving harmonic and reactive power problems indicated the superiority and practicality of the proposed design methods.      

基于GIS和差分进化粒子群的变电站选址方法

文中提出了一种基于地理信息系统(GIS)和差分进化改进粒子群的配电网变电站优化选址方法。该方法利用GIS确定变电站数量,基于变电站投资运行费用建立有约束条件的目标函数,采用粒子群算法进行变电站选址优化。针对粒子群算法易陷入局部最优且收敛速度慢的问题,借助差分进化引入两个变异因子,在提升粒子群算法收敛速度的同时,避免其陷入局部最优。算例分析结果表明,该方法具有较好的寻优能力和收敛特性,能够有效实现变电站选址优化。     

Craziness based particle swarm optimization algorithm for IIR system identification problem

In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.     

基于人工鱼群算法的IIR数字滤波器设计

将人工鱼群算法（AFSA）用于IIR数字滤波器设计，建立了相应的优化模型，给出了简化的人工鱼群算法及其实现步骤。最后，将该算法用于低通、带通IIR数字滤波器的设计，并与粒子群算法进行了比较。仿真结果证明了AFSA的有效性，并且具有算法灵活、简单，全局收敛性好。收敛速度快的优点。     

粒子群优化算法在FIR数字滤波器设计中的应用

本文针对有限脉冲响应(FIR)数字滤波器的设计实质上是一个多参数优化问题,提出了一种用粒子群优化算法(PSO)设计FIR数字滤波器的方法.首先将滤波器的设计问题转化为滤波器参数的优化问题,然后利用粒子群优化算法对整个参数空间进行高效并行搜索以获得参数的最优化.FIR数字低通、带通滤波器设计实例证明了该方法的有效性和优越性.     

Transmission Line Model Based CAD of Multilayer Dielectric Filters

A systematical procedure for multilayer dielectric filter design is introduced here based on the transmission line model (TLMBCAD). By this procedure, low-pass, high-pass, and band-pass filters can be designed in the same way. The transmission line model works well and is time efficient not only for normal incidence of linearly polarized wave but also for oblique incidence wave with circular polarization. Design examples are given for low-pass and band-pass filters. Simulation results show that the method developed in this paper is valuable for the engineering design of multilayer dielectric filters.     

Optimal Design of 2D FIR Filters with Quadrantally Symmetric Properties Using Fractional Derivative Constraints

In this article, an optimal design of two-dimensional finite impulse response (2D FIR) filter with quadrantally even symmetric impulse response using fractional derivative constraints (FDCs) is presented. Firstly, design problem of 2D FIR filter is formulated as an optimization problem. Then, FDCs are imposed over the integral absolute error for designing of the quadrantally even symmetric impulse response filter. The optimized FDCs are applied over the prescribed frequency points. Next, the optimized filter impulse response coefficients are computed using a hybrid optimization technique, called hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA). Further, FDC values are also optimized such that flat passband and stopband frequency response is achieved and the absolute \(L_1\)-error is minimized. Finally, four design examples of 2D low-pass, high-pass, band-pass and band-stop filters are demonstrated to justify the design accuracy in terms of passband error, stopband error, maximum passband ripple, minimum stopband attenuation and execution time. Simulation results have been compared with the other optimization algorithms, such as real-coded genetic algorithm, particle swarm optimization and gravitational search algorithm. It is observed that HPSO-GSA gives improved results for 2D FIR-FDC filter design problem. In comparison with other existing techniques of 2D FIR filter design, the proposed method shows improved design accuracy and flexibility with varying values of FDCs.     

Synthesis of linear antenna arrays using Jaya,self-adaptive Jaya and chaotic Jaya algorithms

This work aims to show the effectiveness of a recently proposed population-based optimization algorithm known as Jaya algorithm and its variants named as self-adaptive Jaya algorithm (SJaya) and Chaotic-Jaya (CJaya) algorithm to synthesize linear antenna arrays which are widely used in the communication systems. Three case studies of synthesis of linear antenna arrays are formulated by considering different topologies. In addition, two case studies of synthesis of dipole antenna arrays are formulated and all the case studies are solved using Jaya, SJaya and CJaya algorithms. The results of Jaya, SJaya and CJaya algorithms are compared with those of cat swarm optimization (CSO) algorithm, particle swarm optimization (PSO), Cauchy mutated cat swarm optimization (CMCSO) algorithm, harmony search based differential evolution algorithm (HSDEA), dynamic differential evolution algorithm (DDE), improved genetic algorithm (IGA), modified real genetic algorithm (MGA) and accelerated particle swarm optimization (APSO) algorithm. The Jaya, SJaya and CJaya algorithms achieved a better side lobe level suppression as compared to the other optimization algorithms while maintaining the vital antenna parameters within permissible limits.     

基于自适应变异粒子群优化算法的自适应滤波器设计

本文提出了设计一种基于自适应变异粒子群优化算法的振动信号的自适应滤波模型，然后重点研究了自适应数字滤波器设计的粒子群优化算法及其实现步骤。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。     

Computer aided design for new microwave filter topologies for spatial applications in Ka band

This paper presents, in the first part, an original topologie of dielectric resonators for multilayer filter applications in Ka band. The resonant element is defined by a partially metallized dielectric plate enclosed in a parallelepipedic cavity. This topology allows a high integration in a planar environment type and presents high electrical performances too. It is easily manufactured, and suitable for high frequencies filtering and power applications. In order to realize multipole filters without tuning, a direct electromagnetic optimization method developed in our laboratory is applied. This method combines a electromagnetic software based on the finite element method, and a specific one to establish the identified coupling matrix deducted from the filter responses. Some experiments are performed to verify the theoretical design. In the second part, we consider a new global function which combines filtering and radiating characteristics. For the first time, an opened tuning less two-pole filter defined by two superposed resonators coupled by a metallic iris is designed. Its filtering and radiating functions are optimized in the same time to present required electrical performances.     

一种改进的灰狼优化算法

灰狼优化算法是最近提出的一种较有竞争力的优化技术.然而,它的位置更新方程存在开发能力强而探索能力弱的缺点.受差分进化和粒子群优化算法的启发,构建一个修改的个体位置更新方程以增强算法的探索能力;受粒子群优化算法的启发,提出一种控制参数a随机动态调整策略.此外,为了提高算法的全局收敛速度,用混沌初始化方法产生初始种群.采用18个高维测试函数进行仿真实验,结果表明：对于绝大多数情形,在相同最大适应度函数评价次数下,本文算法的性能明显优于标准灰狼优化算法.     

A new method for designing multiplierless two-channel filterbank using shifted-Chebyshev polynomials

This paper presents an efficient design method for a digital multiplierless two-channel filterbank using the shifted-Chebyshev polynomials and common sub-expression elimination (CSE) algorithm for reducing hardware requirements such as adders and multipliers. For designing a two-channel filterbank, the design problem is constructed as minimization of integral mean square error between the desired and designed response of a prototype filter in the passband and stopband. For controlling the performance in passband and stopband, two parameters (K_P, and K_S) are used, whose optimum values are determined by swam optimization techniques such as differential evolution algorithm, artificial bee colony optimization, particle swarm optimizations, cuckoo search algorithm and hybrid method using a fitness function, constructed by perfect reconstruction condition of a filterbank. The number of polynomials used for approximation depends upon the order of a prototype filter. A new hybrid CSE is proposed for further reduction of hardware requirement. A comparative study of various CSE techniques such as horizontal, vertical and proposed hybrid CSE is also made. Numerical examples illustrate the effectiveness of the proposed algorithm in the reduction of adders with comparisons accomplished using existing methods. It has been found that almost 43% adder gain can be achieved when a filter is designed with N = 32 and wordlength (WL) as 12 using proposed methodology.     

Structural Parameter Optimization of Multilayer Conductors in HTS Cable

In this paper, the design optimization of the structural parameters of multilayer conductors in high temperature superconducting （HTS） cable is reviewed. Various optimization methods, such as the particle swarm optimization （PSO）, the genetic algorithm （GA）, and a robust optimization method based on design for six sigma （DFSS）, have been applied to realize uniform current distribution among the multilayer HTS conductors. The continuous and discrete variables, such as the winding angle, radius, and winding direction of each layer, are chosen as the design parameters. Under the constraints of the mechanical properties and critical current, PSO is proven to be a more powerful tool than GA for structural parameter optimization, and DFSS can not only achieve a uniform current distribution, but also improve significantly the reliability and robustness of the HTS cable quality.     

Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization

The management of the uncertainties over data is an urgent problem of novel applications such as cyber-physical system, sensor network and RFID data management. In order to adapt the characteristics of evolving over time of sensor data in real-time location tracing service based on RFID, a measuring algorithm for the Uncertainty of RFID Data-PPMU (a particle filter and particle swarm optimization-based measuring uncertainty algorithm for RFID Data) is proposed in this paper. PPMU can change the number of samples adaptively on the basis of K–L distance to adapt the evolution of RFID data, and PPMU introduces an improved PSO (particle swarm optimization) method to enhance the efficiency of re-sampling phase of SIRPF (sequential importance re-sampling particle filter). Meanwhile, PPMU defines a fitness function base on Conventional Weighted Aggregation for PSO that balances the importance between the priori density and likelihood density to detect the most optimal samples among candidate sample sets. It provides a measurement with confidence factor for initial tuples in the probability RFID database. Experiments on real dataset show the proposed method can effectively measure the underlying uncertainty over RFID data. Compared with existing algorithms, PPMU can be further improved particle degradation and particle impoverishment problem.     

Design and performance of adaptive systems based on structured stochastic optimization strategies

The theory and design of linear adaptive filters based on FIR filter structures is well developed and widely applied in practice. However, the same is not true for more general classes of adaptive systems such as linear infinite impulse response adaptive filters (MR) and nonlinear adaptive systems. This situation results because both linear IIR structures and nonlinear structures tend to produce multi-modal error surfaces for which stochastic gradient optimization strategies may fail to reach the global minimum. After briefly discussing the state of the art in linear adaptive filtering, the attention of this paper is turned to MR and nonlinear adaptive systems for potential use in echo cancellation, channel equalization, acoustic channel modeling, nonlinear prediction, and nonlinear system identification. Structured stochastic optimization algorithms that are effective on multimodal error surfaces are then introduced, with particular attention to the particle swarm optimization (PSO) technique. The PSO algorithm is demonstrated on some representative IIR and nonlinear filter structures, and both performance and computational complexity are analyzed for these types of nonlinear systems.     

自适应分解式多目标粒子群优化算法

为了提高多目标粒子群优化算法解的分布性,文中提出了一种自适应分解式多目标粒子群优化算法（Adaptive Multiobjective Particle Swarm Optimization based on Decomposed Archive,AMOPSO-DA）.首先,设计了一种基于优化解空间分布信息的外部档案更新策略,有效提升了AMOPSO-DA的空间搜索能力;其次,提出了一种基于粒子进化方向信息的飞行参数调整方法,有效平衡了AMOPSO-DA的探索和开发能力.最后,将提出的AMOPSO-DA应用于多目标优化问题,实验结果表明,文中提出的AMOPSO-DA能够获得分布性较好的优化解.     

An effective parameter extraction method based on memetic differential evolution algorithm

This paper provides an effective method for parameter extraction of microelectronic devices and elements. A novel method, memetic differential evolution (MDE) algorithm, is proposed in this paper. By combining differential evolution (DE) algorithm, mutations in immune algorithm (IA), and special operators for parameter extraction, MDE possesses characteristics of high accuracy, stability, generality, and efficiency. The effectiveness of the method has been shown by two typical examples, including small-signal equivalent circuit models for an AlGaN/GaN HEMT device up to 40 GHz, as well as an equivalent circuit model for on-chip differential spiral inductors. In both cases, the initial values and parameter ranges of the elements in the equivalent circuits are hard to determine in optimization. The results and comparisons with Levenberg-Marquardt (LM) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm and canonical DE algorithm, demonstrate the superiority of MDE in terms of accuracy and generality.     

一种基于多样性信息和收敛度的多目标粒子群优化算法

为了提高多目标粒子群算法优化解的多样性和收敛性,提出了一种基于多样性信息和收敛度的多目标粒子群优化算法（Multiobjective Particle Swarm Optimization based on the Diversity Information and Convergence Degree,dicdMOPSO）.首先,利用非支配解多样性信息评估知识库中最优解的分布状态,设计出一种全局最优解选择机制,平衡了种群的进化过程,提高了非支配解的多样性和收敛性;其次,基于种群多样性信息设计出一种飞行参数调整机制,增强了粒子的全局探索能力和局部开发能力,获得了多样性和收敛性较好的种群.最后,将dicdMOPSO应用于标准测试函数测试,实验结果表明,dicdMOPSO与其他多目标算法相比不仅获得了多样性较高的可行解,而且能够较快的收敛到Pareto前沿.