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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进化和淘汰策略以改善算法的全局和局部搜索能力,从而加快算法的寻优速率和收敛速度。仿真结果表明:与基于PSO和基于GA的检测算法相比,GPSO的检测算法能够很大程度减少种群规模和迭代次数。而与最优的最大似然译码算法相比,GPSO检测算法能够在计算复杂度和误码性能之间获得很好的折中。 相似文献
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Electromagnetism‐like method tuned constant modulus algorithm for blind detector in multicarrier CDMA system
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Ho‐Lung Hung 《International Journal of Communication Systems》2014,27(2):233-247
This paper presents an efficient meta‐heuristic algorithm based on electromagnetism‐like method, which has been successfully implemented in multiuser detection problems. The contribution revisits blind multiuser detection for multicarrier code division multiple access systems using a novel combined adaptive step‐size constant modulus algorithm (CMA) and electromagnetism‐like method scheme. To work around potentially computational intractability and improved the capability of suppressing multiple access interference (MAI) for Multicarrier CDMA System, the proposed scheme exploits heuristics in consideration of both global and local exploration of the step size of the CMA. Simulation results obtained confirm that faster convergence and desirable BER performance with low computational complexity can be achieved with electromagnetism‐like method based CMA scheme, compared with the previous step‐size CMA scheme, genetic algorithm, and particle swarm optimization with CMA scheme. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far-field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e., particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. The particle swarm optimizer shares the ability of the genetic algorithm to handle arbitrary nonlinear cost functions, but with a much simpler implementation it clearly demonstrates good possibilities for widespread use in electromagnetic optimization. 相似文献
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针对现有医学图像中存在有采集后图像质量不高、图像过暗等现象,对遗传算法中的选择、交叉、变异特性进行研究,同时结合粒子群优化、禁忌搜索及模糊增强算法,提出一种基于改进混合遗传的医学图像模糊增强方法.该方法通过对传统遗传算法改进,将粒子群优化思想及粒子空间对称分布原理引入以改善遗传算法缺乏明确的目标指向性、“突变”性过高的现象,并且为有效降低粒子的同一位置二次搜索,在算法执行过程中加入了禁忌搜索算法.最后,通过与模糊增强算法相结合,并设置二维方向寻优,可自适应的同时寻找到两个模糊参数Fp、Fe最优值,完成医学图像的模糊增强.实验结果表明,改进后算法可有效改善过暗医学CT图像的质量,增强效果较好. 相似文献
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为了提高基于反向传输(back propagation,BP)神经网络的电离层foF2预测的精度,采用了一种改进粒子群优化神经网络的方法,对BP网络的初始权值进行优化,防止出现神经网络训练中的局部最优.通过比较基于粒子群优化的神经网络预测结果与遗传算法优化的神经网络预测结果,我们发现对于BP神经网络,两种方法都有很好的性能.此外,和电离层经验模型国际参考电离层模型(international reference ionosphere 2016,IRI2016)结果进行对比,结果表明,本文提出的自适应变异粒子群(adaptive mutation particle swarm optimization,AMPSO)优化神经网络能有效提高foF2的预测精度,并在低纬地区有更好的预测效果. 相似文献
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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. 相似文献
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Degan Zhang Jiaxu Wang Hongrui Fan Ting Zhang Jinxin Gao Peng Yang 《International Journal of Communication Systems》2021,34(1):e4647
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. 相似文献
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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. 相似文献
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车间调度问题是广泛存在于现实生活中的经典算法规划问题。好的生产调度系统有利于提高企业工作效率及降低企业成本,是工业生产的核心竞争力。粒子群算法因为强大的智能规划能力而被广泛用于车间调度问题当中。文章在原有标准粒子群算法基础上,引入模拟退火机制及遗传算法中交叉变异策略形成的混合粒子群优化算法,并在更具有实际生产环境的动态车间调度中模拟应用,与遗传算法、离散粒子群算法进行比较,具有较强优势。 相似文献
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该文将联姻策略应用在粒子群算法中,提出一种并行分阶段的基于粒子群优化算法的盲信号分离方法(PPSO-GRADS)。该算法具有收敛速度快,分离精度高的特点。通过仿真证明该算法比未使用联姻策略的粒子群算法有更好的性能,在收敛速度和分离效果上比传统的梯度算法,遗传算法都有较明显的改善。 相似文献
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将基于粒子群算法的支持向量机与半监督学习理论相结合,提出了粒子群算法支持向量机的半监督回归模型。针对典型的实验数据集进行实验,并将实验结果与常规的遗传算法支持向量机和粒子群支持向量机模型进行对比。实验结果表明,粒子群算法支持半监督回归模型明显提高了回归估计的精度。 相似文献
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DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization
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Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, chaotic factor and crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO). 相似文献
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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。 相似文献
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受并行遗传算法的启发,文章设计和实现了一种基于环形结构带缓存器模型的并行微粒群算法。它基于一种单向环结构的拓扑连接,可以保证优良粒子在子种群问的扩散,丰富种群的多样性。仿真实验的结果表明.该并行算法不仅有效地提高了求解效率,而且在一定程度上改善了早熟现象,算法的各项性能与微粒群算法相比有了很大提高。 相似文献
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