Particle Swarm Optimization and Differential Evolution for model-based object detection

Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition.The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation.In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subject's posture in space.Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIA? CUDA computing architecture.     

A new clustering method based on the bio-inspired cuttlefish optimization algorithm

Most of the well-known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio-inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K-means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA-Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.     

LADPSO: using fuzzy logic to conduct PSO algorithm

Optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optimization problems and their growing complexity, we need to improve and develop theoretical and practical optimization methods. Stochastic population based optimization algorithms like genetic algorithms and particle swarm optimization are good candidates for solving complex problems efficiently. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by the social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this paper, a new method for improving PSO has been introduced. The Proposed method which has been named Light Adaptive Particle Swarm Optimization is a novel method that uses a fuzzy control system to conduct the standard algorithm. The suggested method uses two adjunct operators along with the fuzzy system in order to improve the base algorithm on global optimization problems. Our approach is validated using a number of common complex uni-modal/multi-modal benchmark functions and results have been compared with the results of Standard PSO (SPSO2011) and some other methods. The simulation results demonstrate that results of the proposed approach is promising for improving the standard PSO algorithm on global optimization problems and also improving performance of the algorithm.     

Measuring the convergence and diversity of CDAS Multi-Objective Particle Swarm Optimization Algorithms: A study of many-objective problems

The interest for many-objective optimization has grown due to the limitations of Pareto dominance based Multi-Objective Evolutionary Algorithms when dealing with problems of a high number of objectives. Recently, some many-objective techniques have been proposed to avoid the deterioration of these algorithms' search ability. At the same time, the interest in the use of Particle Swarm Optimization (PSO) algorithms in multi-objective problems also grew. The PSO has been found to be very efficient to solve multi-objective problems (MOPs) and several Multi-Objective Particle Swarm Optimization (MOPSO) algorithms have been proposed. This work presents a study of the behavior of MOPSO algorithms in many-objective problems. The many-objective technique named control of dominance area of solutions (CDAS) is used on two Multi-Objective Particle Swarm Optimization algorithms. An empirical analysis is performed to identify the influence of the CDAS technique on the convergence and diversity of MOPSO algorithms using three different many-objective problems. The experimental results are compared applying quality indicators and statistical tests.     

基于改进粒子群算法的无人机路径规划

针对传统粒子群算法PSO求解无人机路径规划问题时存在极易陷入局部最优的问题,在PSO算法中引入细菌觅食算法BFO的趋化操作、迁徙操作,以提高其寻优能力。首先根据无人机飞行环境建立三维高程环境模型,并使用路径长度代价、障碍危险代价和航迹高程代价来构造适应度函数;然后在分析了粒子群算法和细菌觅食算法原理及特点的基础上,给出了算法的改进方法及其具体流程。最后,通过Matlab仿真验证表明：混合算法有效改善了粒子群算法的缺陷,在进行无人机路径规划时,相比于传统PSO算法,混合算法寻优精度和稳定性有明显改善。     

Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures.     

QPSO算法优化的非线性观测器设计方法研究

具有量子行为的粒子群优化算法(Quantum-behavedParticleSwarmOptimization,简称QPSO)是继粒子群优化算法(ParticleSwarmOptimization,简称PSO)后,最新提出的一种新型、高效的进化算法。论文在研究基于PSO算法的非线性观测器基础上,提出了一种基于QPSO算法的非线性观测设计方法。以vanderPol系统为例进行了仿真实验,其基本思想是将非线性连续时间系统的状态估计问题转换为非线性函数的在线优化问题,然后利用PSO或QPSO算法获得系统状态的最优估计。仿真结果显示了基于QPSO算法的非观测器比基于PSO算法的非线性观测器的性能更优越。     

基于动态概率变异的Cauchy粒子群优化

介绍了标准粒子群优化（SPSO）算法,在两种粒子群改进算法Gaussian Swarm和Fuzzy PSO的基础上提出了Cauchy粒子群优化（CPSO）算法,并将遗传算法中的变异操作引入粒子群优化,形成了动态概率变异Cauchy粒子群优化（DMCPSO）算法。用3个基准函数进行实验,结果表明,DMCPSO算法性能优于SPSO和CPSO算法。     

基于全局层次的自适应QPSO算法

阐明了具有量子行为的粒子群优化算法理论（QPSO）,并提出了一种基于全局领域的参数控制方法。在QPSO中引入多样性控制模型,使PSO系统成为一个开放式的进化粒子群,从而提出了自适应具有量子行为的粒子群优化算法（AQPSO）。最后,用若干个标准函数进行测试,比较了AQPSO算法与标准PSO（SPSO）和传统QPSO算法的性能。实验结果表明,AQPSO算法具有强的全局搜索能力,其性能优于其它两个算法,尤其体现在解决高维的优化问题。     

新的基于粒子群优化的正交频分复用系统盲频偏估计算法

针对正交频分复用(OFDM)系统频偏估计问题,提出了一种基于粒子群优化(PSO)的盲频偏估计算法。首先,根据频偏估值重建的接收信号和实际接收到的信号误差最小原则构造了盲频偏估计的数学模型,并推导出了代价函数;然后,利用粒子群优化算法强大的随机并行全局搜索能力,通过最小化代价函数估计频偏。仿真比较了常系数、微分递减两种惯性权重策略PSO算法的频偏估计性能,并与最小输出方差、黄金分割盲频偏估计算法进行了比较分析。仿真结果表明,所提算法精度高,同一信噪比下较同类算法大约有一个数量级的提升,且不受调制类型和频偏估计范围(-0.5,0.5)的限制。     

基于改进粒子群算法的Hammerstein模型辨识

提出辨识非线性Hammerstein模型的新方法。将非线性系统的辨识问题转化为参数空间上的函数优化问题,采用粒子群算法获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出采用速度变异粒子群对整个参数空间进行搜索得到系统参数的最优估计。仿真结果验证了该方法的有效性。     

一种混合粒子群算法的贝叶斯网络优化模型

传统的粒子群优化算法通过群体中粒子间的合作和竞争进行群体智能指导优化搜索,算法收敛速度快,但较易陷入局部较优值,进入早熟状态。为了解决这个问题,提出了一种混合粒子群算法的贝叶斯网络优化模型,它可以通过当前所选择的较优解群构造一个贝叶斯网络和联合概率分布模型,利用这个模型进行采样得到更优解,用其可随机替换掉PSO中的一些粒子或个体最优解;同时利用粒子群算法对当前选择出的较优解群进行深度搜索,并将得到的最优解融入到较优解群中。分析可知,该方法可以提高算法有效性和可靠性。     

基于粒子群优化算法的无人机航迹规划

提出一种基于粒子群优化算法的无人机航迹规划方法,利用粒子群优化算法,在等效数字地图中实现单个目标点的无人机航迹规划,并对算法性能进行仔细分析,仿真结果表明,该方法能够快速有效地完成航迹规划任务,得到满意的三维航迹。     

Generation of Particle Swarm Optimization algorithms: An experimental study using Grammar-Guided Genetic Programming

Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-Guided Genetic Programming (GGGP) algorithms, in particular, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. Although GGGP algorithms have been largely used in other contexts, they have not been deeply investigated in the generation of PSO algorithms. Thus, this work applies GGGP algorithms in the context of PSO algorithm design problem. Herein, we performed an experimental study comparing different GGGP approaches for the generation of PSO algorithms. The main goal is to perform a deep investigation aiming to identify pros and cons of each approach in the current task. In the experiments, a comparison between a tree-based GGGP approach and commonly used linear GGGP approaches for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-the-art optimization algorithms, and it achieved competitive results.     

基于QPSO的图像融合算法的研究*

提出了一种基于量子行为的粒子群优化算法(QPSO)的图像融合方法.将图像融合问题归结为最优化问题,采用了QPSO算法进行优化.QPSO不仅参数个数少,其每一个迭代步的取样空间能覆盖整个解空间,因此能保证算法的全局收敛.与PSO算法和遗传算法进行了比较,证明了QPSO算法在图像融合中具有良好的效果.     

Essential Particle Swarm Optimization queen with Tabu Search for MKP resolution

The Particle Swarm Optimization (PSO) algorithm is an innovative and promising optimization technique in evolutionary computation. The Essential Particle Swarm Optimization queen (EPSOq) is one of the recent discrete PSO versions that further simplifies the PSO principles and improves its optimization ability. Hybridization is a principle of combining two (or more) approaches in a wise way such that the resulting algorithm includes the positive features of both (or all) the algorithms. This paper proposes a new heuristic approach such that various features inspired from the Tabu Search are incorporated in the EPSOq algorithm in order to obtain another improved discrete PSO version. The implementation of this idea is identified with the acronym TEPSOq (Tabu Essential Particle Swarm Optimization queen). Experimentally, this approach is able to solve optimally large-scale strongly correlated 0–1 Multidimensional Knapsack Problem (MKP) instances. Computational results show that TEPSOq has outperforms not only the EPSOq, but also other existing PSO-based approaches and some other meta-heuristics in solving the 0–1 MKP. It was discovered also that this algorithm is able to locate solutions extremely close and even equal to the best known results available in the literature.     

基于个体状态的粒子群优化算法

针对标准粒子群算法的种群多样性丧失和算法早熟收敛问题,借鉴自然界中群居动物个体行为的独立性特征,提出粒子的个体状态概念,给出一种基于微粒个体状态和状态迁移的粒子群优化算法。对典型函数测试结果的比较表明,改进后算法的寻优能力明显高于标准粒子群算法。与其他改进算法相比,该算法的寻优能力也较强。     

基于PSO算法的probit模型参数估计

针对二值probit回归模型中的参数估计问题,提出一种基于粒子群优化（PSO）的参数估计算法。该算法采用以最大似然准则作为PSO的适应度函数,建立二值probit回归模型中的参数估计计算模型。数值仿真分析表明,该算法性能较好,回归结果具有较高的拟合优度。     

交叉反向学习和同粒社会学习的粒子群优化算法

针对社会学习粒子群优化（SLPSO）算法存在的优化效率低、收敛速度慢等问题,提出了一种改进的SLPSO算法,即基于交叉反向学习和同粒社会学习的PSO算法（CPPSO）。首先,将最优解随机纵向交叉与一般反向学习以及随机反向学习构建交叉反向学习;然后,以此交叉反向学习策略更新种群中的最优粒子位置,增强探索能力,并克服SLPSO中最优粒子无更新导致效率低下的缺点;最后,对于非最优粒子,与SLPSO采用基于维的社会学习不同,均采用新型基于粒子的社会学习机制,在提高全局搜索能力同时,更提高开采能力和搜索效率。在一组不同维基准函数上优化的实验结果表明,CPPSO的优化性能、搜索效率和普适性大幅度领先于SLPSO和其他先进的PSO改进算法,如交叉搜索PSO （CSPSO）算法、自我调节的PSO （SRPSO）算法、异构综合学习的PSO （HCLPSO）算法和反向学习和局部学习能力的PSO （RLPSO）算法。     

基于粒子群优化算法的Richards模型参数估计和算法有效性

针对Richards模型参数估计较为困难的实际问题,提出将Richards模型的参数估计问题转化为一个多维无约束函数优化问题。结合谷氨酸菌体的实际生长浓度数据,在Matlab 2012b环境中,利用粒子群优化（PSO）算法建立适应度函数,在最小线性二乘意义下估计Richards模型中的4个参数,并建立了拟合的生长曲线和最优值变化曲线。为进一步验证算法有效性,将PSO算法与该模型传统参数估计法中的四点法和遗传算法(GA)进行了比较,以相关指数和剩余标准差作为评价指标。结果表明,PSO算法对Richards模型的拟合效果良好,对模型的参数估计有着很好的适用性。