A hybrid Particle Chemical Reaction Optimization for biological image matching based on lateral inhibition

In this paper, a hybrid Particle Chemical Reaction Optimization (PCRO) algorithm and lateral inhibition is proposed to solve the image matching problem. Lateral inhibition has the ability to enhance the characters of image, which can help to improve the accuracy of image matching. In order to overcome the shortcomings of basic Chemical Reaction Optimization (CRO) algorithm, we improve CRO by proposing PCRO which inspired from the thought of Particle Swarm Optimization (PSO). Comparative experimental results in image matching show that our proposed hybrid method performs much better than other bio-inspired algorithms.     

Hybrid bio-inspired lateral inhibition and Imperialist Competitive Algorithm for complicated image matching

Bio-inspired intelligent algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been applied to solve image matching problems. However, due to high computational complexity and premature convergence problems associated with these methods, they have limitations in defining the global optimal matcher efficiently and accurately. To address these problems, we proposed a hybrid bio-inspired optimization approach, coupling the lateral inhibition mechanism and Imperialist Competitive Algorithm (ICA), to solve complicated image matching problems. With the adoption of the lateral inhibition mechanism, the global convergence of conventional ICA algorithms has been greatly improved. We demonstrate the efficiency and feasibility of the proposed approach by extensive comparative experiments.     

Cauchy Biogeography-Based Optimization based on lateral inhibition for image matching

In this paper, a hybrid method of Cauchy Biogeography-Based Optimization (CBBO) and Lateral Inhibition (LI) is proposed to complete the task of complicated image matching. Lateral inhibition mechanism is adopted for image pre-process to make the intensity gradient in the image contrastively strengthened. Biogeography-Based Optimization (BBO) is a bio-inspired algorithm for global optimization which is based on the science of biogeography, searching for the global optimum mainly through two steps: migration and mutation. To promote the optimization performance, an improved version of the BBO method using Cauchy mutation operator is proposed. Cauchy mutation operator enhances the exploration ability of the algorithm and improves the diversity of population. The proposed LI-CBBO method for image matching inherits both the advantages of CBBO and lateral inhibition mechanism. Series of comparative experiments using Particle Swarm Optimization (PSO), LI-PSO, BBO and LI-BBO have been conducted to demonstrate the feasibility and effectiveness of the proposed LI-CBBO.     

Biological image processing via Chaotic Differential Search and lateral inhibition

In this paper, we propose a hybrid biological image processing approach, which is based on Chaotic Differential Search (CDS) algorithm and lateral inhibition (LI) mechanism. We named this hybrid biological image processing approach as LI-CDS. Differential Search (DS) algorithm is a new bio-inspired optimization algorithm mimicking the migration behavior of an organism, and has been successfully used for solution of coordinate system transformation. The property of chaotic variable is integrated into DS to improve its search strategy so that it can escape from the local optimum. Furthermore, lateral inhibition mechanism, which is verified to have good effects on image edge extraction and image enhancement, is employed to pre-process images involved. In this hybrid biological image processing mechanism, our proposed LI-CDS method incorporates both advantages of chaos theory and lateral inhibition mechanism. Series of comparative experimental results by using LI-CDS, DS, CDS and Particle Swarm Optimization (PSO) demonstrate that the proposed LI-CDS performs better than the other three methods.     

Mode Converter Synthesis by the Particle Swarm Optimization

Particle Swarm Optimization (PSO) is an effective, simple and promising method intended for the fast search in multi-dimensional space [Kennedy and Eberhart, "Particle Swarm Optimization", Proc. of the 1995 IEEE International Conference on Neural Networks, 1995]. Besides special testing problems a number of engineering tasks of electrodynamics were solved by the PSO successfully [Robinson and Rahmat-Samii, "Particle Swarm Optimization in Electromagnetics", IEEE Trans. Antennas Propag., 2004; Jin and Rahmat-Samii, "Parallel Particle Swarm Optimization and Finite-Difference Time-Domain (PSO/FDTD) Algorithm for Multband and Wide-Band Patch Antenna Designs", IEEE Trans. Antennas Propag., 2005]. On the other hand, the scattering matrix technique is a fast and accurate method of mode converter analysis. We illustrate PSO by a number of converter designs developed for high-power microwaves control: a matching horn for output maser section, a corrugated converter of linear-polarized hybrid modes, a TE₀₁ mitre bend.     

基于粒子群算法的多阈值图像分割方法

在对粒子群优化算法的基本原理和方法进行简要概述的基础上,提出了一种基于粒子群的多阈值图像分割算法。算法采用信息熵构建优化目标函数,提出了新的粒子更新准测,并以此对图像进行了多阈值优化搜索。实验表明,该算法不仅能对图像进行正确的分割,而且还具有稳定性高,易于实现,速度快等特点。     

基于微粒群区域搜索和小波评价的差分式自动聚焦

为进一步提高图像法自动聚焦的性能,提出了一种差分式提取图像边缘的方法,并构造了图像清晰度的小波评价函数,同时利用微粒群(PSO)算法对聚焦区域进行快速搜索。首先,介绍了差分式边缘提取方法及其优势,给出了一种评价区域的选取判据以及基于PSO的高效搜索方法;然后,对小波评价函数参数进行了比较分析和优选;最后,与传统方法进行了对比实验。结果表明,由于采用了差分式提取方法以及新的自适应聚焦窗口和评价函数,聚焦曲线较传统方法具有更高的调焦分辨率,PSO算法的使用使聚焦速度提高了约170 ms,聚焦精度约为2.3μm,同时调焦效果不受初始位置的影响。     

Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method

Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.     

基于微粒群区域搜索和小波评价的差分式自动聚焦

为进一步提高图像法自动聚焦的性能,提出了一种差分式提取图像边缘的方法,并构造了图像清晰度的小波评价函数,同时利用微粒群（PSO）算法对聚焦区域进行快速搜索。首先,介绍了差分式边缘提取方法及其优势,给出了一种评价区域的选取判据以及基于PSO的高效搜索方法;然后,对小波评价函数参数进行了比较分析和优选;最后,与传统方法进行了对比实验。结果表明,由于采用了差分式提取方法以及新的自适应聚焦窗口和评价函数,聚焦曲线较传统方法具有更高的调焦分辨率,PSO算法的使用使聚焦速度提高了约170 ms,聚焦精度约为2.3μm,同时调焦效果不受初始位置的影响。     

基于混合粒子群优化算法的DV-Hop定位算法

针对无线传感器网络节点定位技术中DV-Hop算法的不足,利用混合粒子群优化算法对DV-Hop算法的位置估计进行校正,提出了一种CCPDV-Hop算法,该方法在不需要任何额外硬件设备和通信开销基础上,将未知节点定位问题抽象为高维最优化问题,并利用混合粒子群优化算法进行求解。仿真实验结果表明,改进的DV-Hop算法与传统方法相比,定位误差显著下降,定位精度和鲁棒性都有明显提高。     

Finding two-dimensional peaks

Two-dimensional generalization of the original peak finding algorithm suggested earlier is given. The ideology of the algorithm emerged from the well-known quantum mechanical tunneling property which enables small bodies to penetrate through narrow potential barriers. We merge this “quantum” ideology with the philosophy of Particle Swarm Optimization to get the global optimization algorithm which can be called Quantum Swarm Optimization. The functionality of the newborn algorithm is tested on some benchmark optimization problems. The text was submitted by the author in English.     

用粒子群算法校正三片镜系统的像差（特邀）

提出将粒子群算法用于三片镜光学系统的优化设计。设计了关于曲率半径、透镜面之间的距离、玻璃折射率、系统长度等光学系统结构参数的光学评价函数,用此函数作为粒子群算法中的适应度函数,实现了对光学系统的自动寻优。给出了用粒子群算法进行三片镜光学系统设计过程实例,结果证明：用粒子群算法可以设计出球差、子午场曲、子午光线弥散值都很小的三片镜光学系统;并且用该算法进行光学设计不需要知道系统具体的初始结构,克服了现有光学设计软件高度依赖具体初始结构的缺点,可以自由控制结构参数的搜索范围,从而提高光学系统设计的智能化程度。     

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.     

应用粒子群算法进行光学自动设计

将粒子群算法用于光学系统的设计优化中,构造相应的数学模型,并编程实现算法.设计了关于曲率半径r、透镜面之间的距离d和玻璃折射率n等光学系统结构参数的适应度函数,用此函数作为评价函数实现对像差的自动校正.给出用粒子群算法进行光学系统设计的实例.结果证明,粒子群算法可以克服以往光学设计中高度依赖初始结构的缺点,可以自由控制结构参数的搜索范围,从而提高光学系统设计的智能化程度.     

基于自组织聚类和改进粒子群算法的语音转换方法

提出一种基于自组织聚类,并且利用改进粒子群算法确定转换模型参数的语音转换方法.该方法首先基于自组织特征映射网络对特征参数进行聚类,再对每个聚类分别建立转换规则,并且利用柯西变异的粒子群算法确定每个转换规则中的模型参数.与传统的单一转换规则相比,聚类后建立的多转换规则以及利用改进粒子群算法确定参数能够提高映射关系的准确度,避免参数陷入局部最优点。以女声转男声为例,主观测试表明该方法得到的转换语音与目标的相似度提高了27.6%,平均主观意见分(Mean Opinion Score,MOS)提高了0.6,客观测试也表明该方法谱失真最小,与目标的包络更接近.      

Particle swarm optimization applied to automatic lens design

This paper describes a novel application of Particle Swarm Optimization (PSO) technique to lens design. A mathematical model is constructed, and merit functions in an optical system are employed as fitness functions, which combined radiuses of curvature, thicknesses among lens surfaces and refractive indices regarding an optical system. By using this function, the aberration correction is carried out. A design example using PSO is given. Results show that PSO as optical design tools is practical and powerful, and this method is no longer dependent on the lens initial structure and can arbitrarily create search ranges of structural parameters of a lens system, which is an important step towards automatic design with artificial intelligence.     

用粒子群优化算法设计光纤布拉格光栅

用粒子群优化(PSO)算法对二元相位取样光栅进行优化设计.实现了利用二元相位取样光栅对多信道色散和色散斜率的同时补偿.设计得到的二元相位取样光栅的反射谱各信道均匀,时延线性好,带宽、色散量和色散斜率可以根据实际的光纤类型来调整,对折射率调制要求较低.由于只有π相移,因此其制作比多级相位取样光栅相对简单.与其他优化方法相比,粒子群优化算法具有简单、收敛速度快等优点.     

Optical buffer performance enhancement using Particle Swarm Optimization in Ring-Shape-Hole Photonic Crystal Waveguide

Ring-Shape-Hole Photonic Crystal Waveguides (RSHPCW) is a popular structure in designing optical buffers attracted many attentions recently. There are some parameters such as the radii of holes and pillars next to the defect that have significant effects on slow light properties. Consequently, one of the promising methods for effectively slowing the light speed down and controlling dispersion is to optimize these parameters, which is the motivation of this study. Particle Swarm Optimization (PSO) algorithm is one of the best proposed heuristic optimization algorithms in Artificial Intelligence applied to many engineering problem. In this work, this algorithm is employed to find the best values of the aforementioned radii for maximizing Normalized Delay-Bandwidth Product (NDBP) of RSHPCW structure as the first systematic optimizer. Calculation results show that there are 34% and 41% improvement in NDBP and bandwidth compared to the previous works, very substantial achievements in this area.     

一维Renyi熵阈值法中参数的自适应选取

提出了一种自适应选取一维Renyi熵阈值分割法中参数α的方法.该方法以一种图像分割质量评价指标 均匀性测度为适应度函数,利用粒子群算法在参数空间进行优化搜索,从而可以根据具体的图像获得合适的参数,得到最佳的图像分割阈值.结果表明：一般情况下,可以（0,1）范围内搜索最优的α值|当需要更好的分割效果时,可在（0,10）范围内搜索最优的α值.     

Forecasting pedestrian evacuation times by using swarm intelligence

Many models have been developed to provide designers with methods for forecasting the time required for evacuation from various places under a variety of conditions. Particularly for high traffic buildings or buildings of cultural, governmental, or industrial importance, it is of paramount importance to properly evaluate and plan for the necessary evacuation time. To address this need, a number of models for pedestrian simulation, either considering the system as a whole or studying the behavior and decisions of individual pedestrians and their interactions with other pedestrians, have been developed over the years. In this work, a model for evacuation simulation and for estimating evacuation times is proposed. It is inspired by the so-called Particle Swarm Optimization (PSO). The multi-agent-based simulation characteristics of PSO and the way this technique combines individual and collective intelligence make it suitable for this problem. The PSO-based model presented here allows for assessment of the behavioral patterns followed by individuals during a rapid evacuation event. Evaluation of these behaviors can address a variety of public safety concerns, such as architectural design, evacuation protocol definition, and regulation of public space.