基于进化计算的二元序列优化算法研究

具有良好非周期自相关特性二元序列在通信同步、雷达等领域具有广泛的应用。通过对遗传算法、粒子群算法与量子粒子群算法三种进化算法进行对比分析,设计了具有良好非周期自相关特性的二元序列的搜索算法。研究结果表明,粒子群算法的搜索能力优于遗传算法,而量子粒子群算法具有参数少,易于控制的优点,取得了较好的优化结果。     

自适应变异的粒子群优化算法

本文提出了一种新的基于群体适应度方差自适应变异的粒子群优化算法(AMPSO).该算法在运行过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,变异操作增强了粒子群优化算法跳出局部最优解的能力.对几种典型函数的测试结果表明:新算法的全局收搜索能力有了显著提高,并且能够有效避免早熟收敛问题.     

基于自适应粒子群算法的目标定位方法研究

针对现有定位求解算法复杂和标准粒子群算法易陷入局部最优的缺点,提出了一种基于自适应粒子群算法的目标定位方法.该方法在迭代过程中指数更新惯性权重,择优选择粒子,并根据种群适应度方差值自适应地调整变异概率的大小,增强算法跳出局部最优的能力.仿真结果表明该方法能有效地提高目标的定位精度,在随机噪声干扰方差为0.5的条件下,定位均方误差不超过0.8m.     

求解多输入多输出检测新算法——遗传粒子群优化

设计两种基于粒子群优化算法(PSO)和基于遗传算法(GA)的多输入多输出(MIMO)系统检测算法.提出一种新的融合GA和PSO进化机制的遗传粒子群进化(GPSO)算法,并将其应用于MIMO系统检测问题求解.新算法改善了初始化种群,并将每一代粒子划为精英粒子、次优粒子和糟糕粒子三部分,对这三种粒子分别采用极值扰动、PSO...     

Investigation on the evolutionary algorithms with their applications in MIMO detecting systems

In this paper, with the purpose of integrating the advantages of both the genetic algorithm and the particle swarm optimization, a new genetic particle swarm optimization (GPSO) algorithm is proposed. Furthermore, these three evolutionary algorithms are successfully applied to address the MIMO detection problem. Simulation results reveal that the GPSO‐based detection algorithm takes much less population size and iteration number when compared with the particle swarm optimization‐based detection method and the genetic algorithm‐based detection method. Besides, when compared with the optimal maximum likelihood detection method, the GPSO‐based detection algorithm can strike a much better balance between the BER performance and the computational complexity. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

量子行为粒子群算法在图像恢复中的应用

遗传算法等智能搜索技术避免了图像恢复方法中存在的较多约束和计算量过大的问题,但遗传算法存在“过早收敛”现象。作为一种新的智能优化算法-量子行为粒子群优化算法,在全局收敛性和稳定性上有较好的表现。文章提出了一种基于量子行为粒子群算法的图像恢复方法,并与基于标准遗传算法的图像恢复进行了比较。仿真结果表明,该算法可使图像恢复结果和效率得以较大的改善和提高,具有推广应用价值。     

Blind signals separation with genetic algorithm and particle swarm optimization based on mutual information

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sources. Simulations results demonstrate that the proposed method for employing fitness function has rapid convergence, simplicity and a more favorable signal to noise ratio for separation tasks based on particle swarm optimization and continuous genetic algorithm than binary genetic algorithm. Also, particle swarm optimization enjoys shorter computation time than the other two algorithms for solving these optimization problems for multiple sources.     

基于改进斑点鬣狗优化算法的红外图像分割

针对斑点鬣狗优化算法（spotted hyena optimizer,SHO）容易陷入局部最优解、求解质量低等缺点,本文提出使用Lévy飞行和单纯形搜索算法改进SHO（spotted hyena optimizer based on simplex method and Lévy flight, Lévy_SM_SHO）。将Lévy_SM_SHO与Lévy飞行斑点鬣狗优化算法（spotted hyena optimizer based on Lévy flight, Lévy_SHO）、单纯形搜索斑点鬣狗优化算法（spotted hyena optimizer based on simplex method, SM_SHO）和SHO在测试函数上结果进行对比,实验证明改进算法能够取得较好的优化结果。并将Lévy_SM_SHO算法用于红外图像阈值分割问题,通过与粒子群算法（particle swarm optimization, PSO）分割结果对比,证明Lévy_SM_SHO算法能够取得较好的阈值分割结果。     

基于GA-PSO融合算法的二自由度PID参数优化

在工业过程控制中,PID参数调节直接影响工业生产的质量和效率。针对PID参数调节难这一问题,文中提出了一种将遗传算法和粒子群算法相结合的智能融合算法,并将该算法应用于二自由度PID参数的优化中。该算法在遗传算法的变异算子中引入粒子群算法,充分发挥两种单一智能算法的优点,并弥补了两者的缺点。算例仿真验证结果显示,该算法可以很好的应用于PID参数优化,且在调节PID参数的过程中具有优良的性能指标数值,在目标值跟踪特性和外扰动抑制特性上具有更好的控制效果。     

基于混合PSO小波网络的网络异常检测

提出一种用新型的进化学习算法训练的小波神经网络(WNN).这种新型的进化学习算法是基于粒子群算法(PSO)和共轭下降法(CG)提出的.以往,将粒子群算法用于神经网络的训练一般是可行的.因为粒子群算法相比于其他的优化算法,具有相对简单的结构和快速的收敛速度,然而,由于粒子的搜索坍塌速度过快而导致粒子停滞这种潜在的危险.粒子的持续停滞使搜索结果很难达到全局最优,甚至会陷入局部最优.为了克服粒子群算法缺点提出了改进的混合算法.通过对KDD 99数据集的实验表明,利用新型混合算法训练的小波神经网络对于异常检测具有很高的异常检测率并且又较低的误判率.可见,该方法对于网络异常检测是有效的.     

New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system

Traffic flow forecasting is one of the essential means to realize smart cities and smart transportation. The accurate and effective prediction will provide an important basis for decision‐making in smart transportation systems. This paper proposes a new method of traffic flow forecasting based on quantum particle swarm optimization (QPSO) strategy for intelligent transportation system (ITS). We establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing algorithm is applied to the quantum particle swarm algorithm to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network is used to obtain the required data. In addition, in order to compare the performance of the algorithms, a comparison study with other related algorithms such as QPSO radial basis function (QPSO‐RBF) is also performed. Simulation results show that compared with other algorithms, the proposed algorithm can reduce prediction errors and get better and more stable prediction results.     

布谷鸟粒子群优化算法的多目标任务调度

高效的调度方法促使云计算更快更好地服务,一般采用优化算法来解决云计算中的调度问题。将布谷鸟搜索(CS)和粒子群优化(PSO)两种算法相结合,提出多目标布谷鸟粒子群优化算法(MO-CPSO),主要目的是提高云计算的服务质量。使用Cloudsim仿真工具对MO-CPSO算法的性能进行了评估。仿真结果表明,与CS、ACO和Min-Min算法相比,MO-CPSO算法使makespan、开销和截止时间违背率均最小。     

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.     

改进的粒子群优化算法

提出了改进的粒子群优化算法。基于4个不同的基准函数对所提算法与1995年Kennedy和Eberhart提出的常规PSO作了比较。PSO最初是受到如鸟或鱼等生物群体的社会行为的启发而提出的,每一个体依照自身及群体的过去解决问题的最好办法来调整自己的最佳位置,通过重复这一过程来得出最佳值。这里提出的改进的PSO的关健之处在于:如果一个新的位置确实得到了改善,则每一个体就调整它的位置;如果不是这样,就根据概率来做出决定。这一策略是既避免盲目跳转又避免只简单地跳转到好的新位置而陷入局部最优。模拟结果表明改进的PSO总能比PSO找到更好的解决方法。     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

基于文化粒子群算法的广义OPM测向

在引入合成导向矢量和合成导向矩阵的基础上建立了一种更通用的阵列数据模型,提出了广义正交传播算子测向算法。该方法在不损失阵列孔径的前提下可有效估计相干信源和独立信源。为快速求解所提基于四阶累积量的广义OPM算法,在文化算法中使用和粒子群演进进化机制,提出了一种多维搜索的文化粒子群算法。Monte-Carlo仿真试验证明了所设计的测向算法可有效解决使用高阶累积量不能直接测相干信源的局限,所提算法在检测性能上与现有一些算法比较有较大的优势。     

基于余弦距离的多目标粒子群优化算法

针对粒子群优化算法具有的个体分布不均匀以及重复个体较多等缺陷,提出了一种基于余弦距离的多目标粒子群优化算法,该算法根据外部精英存储策略,利用余弦距离排挤机制来选取最分散的粒子,扩大 Pareto最优解集的收敛性和多样性,增强算法的全局寻优能力。通过采用标准多目标优化问题ZDTl～ZDT3进行仿真实验与粒子群算法、混沌粒子群算法、基于拥挤距离的多目标优化算法对比表明,该算法在Pareto前沿的收敛性和多样性方面均优于基于拥挤距离排挤机制,并具有较高的效率     

基于拟生态优化算法的CDMA多用户检测方法

拟生态优化算法是一类模拟自然生态系统运行机制,求解复杂优化问题的智能计算方法,其中的蚁群算法和粒子群算法是较新出现的两种具有不同特点的方法。该文研究基本蚁群算法和离散粒子群算法,并结合CDMA多用户检测问题,改变算法的搜索机制,提出两种CDMA多用户检测的方法。从理论分析以及实验仿真的角度对比两种方法,表明两种方法的计算复杂度低且可以得到较好误码率性能,同时又各有特点。     

序列图象编码中位移估值新算法—二维遗传优化算法

本文分析了块匹配算法中几种快速搜索算法存在的问题,基于全局最优化思想提出一种新的位移估值算法——二维遗传优化算法。对帧间差值信号采用相同的编码方法时,该算法和二维对数搜索算法比较,所需编码比特数减少,恢复图象信噪比有所提高,运算复杂度也得到下降。