Inverse transient radiation analysis in one-dimensional non-homogeneous participating slabs using particle swarm optimization algorithms

Particle Swarm Optimization (PSO) algorithms, including standard PSO, Stochastic PSO, and Multi-Phase PSO, are applied to solve the time-domain inverse transient radiation problems in the present research. Time-resolved transmittance and reflectance signals of four different measuring models serve as the measurement data, which estimate absorption, scattering coefficients, and geometric position within one-dimensional non-homogeneous media by inverse simulation. To check retrieval performances and accuracies of PSO-based approaches, four different inverse transient radiation cases are investigated to deal with one homogeneous layer, two-layer, three-layer, and continuous non-homogenous media. The influences of different searching ranges, swarm sizes, and maximum fly velocities on the fitness function of PSO are discussed. Meanwhile, the effects of measurement errors on the reconstruction accuracy are also investigated. All the results confirm that radiative parameters could be estimated accurately with measurement noise using PSO-based approaches.     

Application of multi-phase particle swarm optimization technique to retrieve the particle size distribution

The multi-phase particle swarm optimization (MPPSO) technique is applied to retrieve the particle size distribution (PSD) under dependent model.Based on the Mie theory and the Lambert-Beer theory, three PSDs, i.e., the Rosin-Rammer (R-R) distribution, the normal distribution, and the logarithmic normal distribution, are estimated by MPPSO algorithm.The results confirm the potential of the proposed approach and show its effectiveness.It may provide a new technique to improve the accuracy and reliability of the PSD inverse calculation.     

基于粒子群算法的稀疏阵列超声相控阵全聚焦成像

为降低相控阵超声检测全聚焦算法的成像数据量及阵列稀疏优化的计算时间,研究了一种用于稀疏阵列全聚焦成像的阵列优化算法,并通过实验对其成像效果进行了验证。针对目前超声相控阵检测的全矩阵采集数据量大、全聚焦算法成像时间长的难点,该文通过构建稀疏阵列,在保证成像质量的同时显著降低成像数据量,提高了全聚焦算法的成像效率。通过以主瓣宽度、旁瓣峰值以及主瓣峰值作为约束条件构建适应度函数,采用粒子群算法得到稀疏阵元位置分布并进行阵元权重修正,并将其用于稀疏全聚焦成像。相比全阵元成像,使用粒子群算法所得的稀疏阵列的阵元个数降低了56.25%、65.62%,数据使用量降低了80.86%、88.18%。在阵列优化方面,相比遗传算法减少了84.86%的计算时间。     

Shooting algorithm and particle swarm optimization based Raman fiber amplifiers gain spectra design

An initial value determination method with a contraction factor for the counter-pumped Raman coupled equations is proposed. This method is used in conjunction with initial guess correction mechanism of Newton's method to construct a new efficient shooting algorithm for the solution of counter-pumped Raman coupled equations. The particle'swarm optimization is used to find the optimal wavelengths and powers for the pumps. By combining the new shooting algorithm and particle swarm optimization a powerful approach to the design of gain spectra for Raman fiber amplifiers is developed. Using this approach a counter-pumped broadband Raman fiber amplifier in C + L-band is designed and optimized. An average on-off gain of 9.3 dB for a bandwidth of 95 nm is obtained using only 4 pumps, with an in-band ripple level of ± 0.7 dB.     

2维静磁场多参量微动粒子群优化算法

提出一种微动粒子群优化算法,针对2维静磁场多参量优化问题,在给出轴上目标轴向磁感应强度分布曲线的前提下,可以得到趋近于该分布曲线的磁结构。该算法分为前后两阶段:第一阶段采用前后试探法(微动),同时也参照最优粒子的信息;第二阶段采用基本粒子群优化算法。微动粒子群优化算法可以发挥多核计算机在工程设计上的潜力,而且即使粒子数目很少,也能不断趋近目标解。     

2维静磁场多参量微动粒子群优化算法

提出一种微动粒子群优化算法,针对2维静磁场多参量优化问题,在给出轴上目标轴向磁感应强度分布曲线的前提下,可以得到趋近于该分布曲线的磁结构。该算法分为前后两阶段:第一阶段采用前后试探法(微动),同时也参照最优粒子的信息;第二阶段采用基本粒子群优化算法。微动粒子群优化算法可以发挥多核计算机在工程设计上的潜力,而且即使粒子数目很少,也能不断趋近目标解。     

Flat gain spectrum design of Raman fiber amplifiers based on particle swarm optimization and average power analysis technique

In this work, the modified particle swarm optimization is used as an optimization tool to determine the set of wavelengths and power levels of pumps that delivers a flat gain spectrum for Raman fiber amplifiers. The average power analysis technique is used as a numerical method to solve the coupled Raman amplifier equations. By combining the modified particle swarm optimization and average power analysis technique an efficient algorithm for the design of flat-gain-spectrum broadband Raman fiber amplifiers is constructed. Application of this algorithm to the design of flat-gain-spectrum broadband Raman fiber amplifiers shows that the design efficiency of the new method is improved by 1-2 orders of magnitude compared with similar implementations previously reported in the literature. A 4-backward-pump gain-flattened Raman amplifier with bandwidth of 100-nm and maximum gain ripple of <1.0 dB is designed to demonstrate the technique.     

Inverse acoustical characterization of natural jute sound absorbing material by the particle swarm optimization method

For modeling of jute as acoustic material, knowledge of its non-acoustical parameters like porosity, tortuosity, air flow resistivity, thermal and viscous characteristic lengths is a prime requisite. Measurement of these non-acoustical parameters is not straightforward and involves a dedicated measurement setup. So in order to overcome this issue, the inverse acoustical characterization can be used. In this paper, the particle swarm optimization method (PSO) is used as an optimization method. This method estimates the non-acoustical parameters of jute material in felt form by minimizing the error between experimental and theoretical sound absorption data. In this work, the impedance prediction models for fibrous materials like Johnson–Champoux–Allard model with rigid and limp frame and Garai–Pompoli model is used for sound absorption coefficient calculation by the transfer matrix method along with the PSO. The inverse estimated non-acoustical parameters for jute material are then compared with estimated and experimentally measured parameters for jute felts. Using these inversely predicted parameters, sound absorption of multilayer sound absorbers is also studied.     

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.     

The solution of inverse radiation problems using an efficient computational technique

In our previous work (Park, Kim, JQSRT 58 (1) (1997) 115), an efficient computational technique for solving the radiative transfer equations for participating media has been devised, which is as accurate as the S₄ method but consumes much less computer time. In the present investigation, we employ this technique to solve an inverse radiation problem of determining the time-varying strength of a heat source, which mimics flames in a furnace, from temperature measurements in three-dimensional participating media where radiation and conduction occurs simultaneously. The present technique is found to identify the strength of the heat source efficiently without a priori information about the unknown function to be estimated.     

Inverse problem for particle size distributions of atmospheric aerosols using stochastic particle swarm optimization

As a part of resolving optical properties in atmosphere radiative transfer calculations, this paper focuses on obtaining aerosol optical thicknesses (AOTs) in the visible and near infrared wave band through indirect method by gleaning the values of aerosol particle size distribution parameters. Although various inverse techniques have been applied to obtain values for these parameters, we choose a stochastic particle swarm optimization (SPSO) algorithm to perform an inverse calculation. Computational performances of different inverse methods are investigated and the influence of swarm size on the inverse problem of computation particles is examined. Next, computational efficiencies of various particle size distributions and the influences of the measured errors on computational accuracy are compared. Finally, we recover particle size distributions for atmospheric aerosols over Beijing using the measured AOT data (at wavelengths λ=0.400, 0.690, 0.870, and 1.020 μm) obtained from AERONET at different times and then calculate other AOT values for this band based on the inverse results. With calculations agreeing with measured data, the SPSO algorithm shows good practicability.     

Modeling of pedestrian evacuation based on the particle swarm optimization algorithm

By applying the evolutionary algorithm of Particle Swarm Optimization (PSO), we have developed a new pedestrian evacuation model. In the new model, we first introduce the local pedestrian’s density concept which is defined as the number of pedestrians distributed in a certain area divided by the area. Both the maximum velocity and the size of a particle (pedestrian) are supposed to be functions of the local density. An attempt to account for the impact consequence between pedestrians is also made by introducing a threshold of injury into the model. The updating rule of the model possesses heterogeneous spatial and temporal characteristics. Numerical examples demonstrate that the model is capable of simulating the typical features of evacuation captured by CA (Cellular Automata) based models. As contrast to CA-based simulations, in which the velocity (via step size) of a pedestrian in each time step is a constant value and limited in several directions, the new model is more flexible in describing pedestrians’ velocities since they are not limited in discrete values and directions according to the new updating rule.     

Improving the performance of a photonic crystal ring-resonator-based channel drop filter using particle swarm optimization method

The optical channel drop filters (CDFs) based on photonic crystals (PCs) are believed to be the essential components for compact photonic integrated circuits and WDM systems. One of the promising designs for a PC-CDF is based on the photonic crystal ring resonators (PCRRs). In this study, different parameters of a PCRR based CDF is optimized by using the particle swarm optimization (PSO) method in conjunction with the numerical method of two-dimensional (2D) finite-difference time-domain (FDTD) in a square lattice dielectric-rod 2D-PC. Hence a PC-CDF with ideal drop efficiency, large free spectral range (FSR) and acceptable quality factor will be proposed.     

The emergence of scaling laws search dynamics in a particle swarm optimization

This paper investigates the search dynamics of a fundamental particle swarm optimization (PSO) algorithm via gathering and analyzing the data of the search area during the optimization process. The PSO algorithm exhibits a distinct performance when optimizing different functions, which induces the emergence of different search dynamics during the optimization process. The simulation results show that the performance is tightly related to the search dynamics which results from the interaction between the PSO algorithm and the landscape of the solved problems. The Lévy type scaling laws search dynamics emerges from the process in which the PSO algorithm shows good performance, while the Brownian dynamics appears after the algorithm has stagnated due to the premature convergence. The Lévy dynamics characterized by a large number of intensive local searches punctuated by long-range transfers is an indicator of good performance, which allows the algorithm to achieve an efficient balance between exploration and exploitation so as to improve the search efficiency.     

基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎,并提出了一种种群自适应粒子群算法,利用粒子群算法调整优化无线电参数,运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法,基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能,满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎     

An adaptive greedy technique for inverse boundary determination problem

In this paper, the method of fundamental solutions (MFS) is employed for determining an unknown portion of the boundary from the Cauchy data specified on parts of the boundary. We propose a new numerical method with adaptive placement of source points in the MFS to solve the inverse boundary determination problem. Since the MFS source points placement here is not trivial due to the unknown boundary, we employ an adaptive technique to choose a sub-optimal arrangement of source points on various fictitious boundaries. Afterwards, the standard Tikhonov regularization method is used to solve ill-conditional matrix equation, while the regularization parameter is chosen by the L-curve criterion. The numerical studies of both open and closed fictitious boundaries are considered. It is shown that the proposed method is effective and stable even for data with relatively high noise levels.     

量子势阱粒子群优化算法的改进研究

为提高量子势阱粒子群优化算法的优化能力, 通过分析目前量子势阱粒子群优化算法的设计过程, 提出了改进的量子势阱粒子群优化算法. 首先, 分别基于Delta势阱、谐振子和方势阱 提出了改进的量子势阱粒子群优化算法, 并提出了基于统计量均值的控制参数设计方法. 然后, 在势阱中心的设计方面, 为强调全局最优粒子的指导作用, 提出了基于自身最优粒子加权平均和动态随机变量的两种设计策略. 实验结果表明, 三种势阱粒子群优化算法性能比较接近, 都优于原算法, 且Delta势阱模型略优于其他两种.     

Solution of the inverse radiative load problem in a two-dimensional system

An inverse radiation analysis is presented for estimating the temperature and the heat load distributions of the heating surface from the temperature and the heat flux measurements of the heated object. The Monte Carlo method is employed to solve the direct radiation problem. The inverse radiation problem is solved using the conjugate gradient and singular value decomposition methods. The measured data are simulated by adding random errors to the exact solution of the direct problem. The effects of the measurement errors on the accuracy of the inverse analysis are investigated. The study shows that the heat load distribution of the heating surface can be estimated accurately for the exact and noisy data. And the conjugate gradient method is better than the singular value decomposition method since the former can obtain more accurate results if the measurement errors are the same.     

Design of broadband hybrid fiber amplifier based on particle swarm optimization algorithm北大核心CSCD

基于铒/镱共掺光纤放大器（erbium-ytterbium doped fiber amplifier, EYDFA）的理论模型和受激拉曼散射效应的分析理论,利用EYDFA和拉曼光纤放大器（Raman fiber amplifier, RFA）的增益谱互补特性,研究并设计了EYDFA与二阶多泵浦RFA相结合的混合放大器结构。为了得到高增益和低平坦度的混合放大器,引入了粒子群算法优化泵浦光波长和功率。仿真结果表明:在不使用增益均衡器的条件下,所设计的混合光纤放大器在输出端得到了近似相等的输出光功率,在90 nm的带宽范围内平均增益为38.78 dB,增益平坦度为1.1 dB,为混合放大器的设计和优化提供了参考。     

基于改进粒子群优化算法的氩原子团簇结构优化

采用改进粒子群优化算法(IPSO)结合Lennard-Jones势对氩原子团簇结构进行优化,得到了氩原子团簇的稳态结构能量。以氩原子团簇Arn（n = 2-14）为例,验证了该方法的有效性。结果表明,应用本文提出的方法可得到对称性良好的团簇结构。与基本粒子群优化算法(BPSO)及遗传算法(GA)相比,改进粒子群优化算法具有更好的收敛特性,能较快地得到氩原子团簇结构的最优解。