含维变异的量子粒子群算法在无功优化中的应用

量子粒子群优化算法(QPSO)避免了粒子群算法(PSO)不能保证收敛到全局最优解这个缺点,认为粒子具有量子的行为,并且可以在整个可行解空间进行搜索。无功优化问题是带有离散变量的非线性、不连续、多约束、多变量的复杂优化问题。本文考虑到优化过程中避免陷入局部最优,应用含维变异QPSO算法并结合动态调整罚函数的方法来解决无功优化问题。并对标准IEEE-30节点系统进行仿真计算,并与QPSO、PSO、GA算法进行了比较,表明该算法能够获得更好的全局最优解。     

量子粒子群算法在钢框架优化设计中的运用

介绍了标准粒子群算法的基本思想,提出了钢框架抗震优化设计的量子粒子群算法,建立了多层钢框架优化设计数学模型,最后通过一个算例验证了该方法的效率和有效性,结果表明该方法科学可行,具有很好的应用前景。     

改进的QPSO算法在电网故障诊断系统中的应用

电网故障诊断系统通常基于建立的解析模型,通过分析保护和断路器的动作信息来推断可能的故障位置,从而识别保护与断路器的故障元件和误动作;根据保护动作原理,构建了一种改进的解析模型,并采用改进的量子粒子群算法对其目标函数进行优化求解;该模型不仅充分考虑到了保护和断路器的误动与拒动、断路器失灵保护等问题,且能辨识告警信息的误报和漏报;实验结果表明改进的算法不仅使故障诊断结果更精确,并能使故障情况很清晰地表示出来,有利于故障的及时恢复,同时使模型的运算速度和稳定性也进一步得到了提高。     

基于改进型象群优化算法的 BSS 方法

为了克服传统群智能算法在求解盲源分离(BSS)问题时收敛速度慢和分离精度差的缺点,提出一种基于改进型象群优化(IEHO)算法的BSS方法.该方法利用独立性原则,融合分离信号的峭度和互信息来构建目标函数.在氏族更新阶段,通过改进算法比例因子并加入邻域搜索,提高了算法搜索方式的多样性;在分离阶段,引入量子粒子群优化策略,提高了算法的全局搜索能力.仿真结果表明,与传统的象群优化算法和粒子群优化算法相比,IEHO算法的寻优效果较好,并成功实现了图像信号和语音信号的盲源分离,分离精度更高,收敛速度更快.     

A Novel Quantum-Behaved Particle Swarm Optimization Algorithm

The efficient management of ambulance routing for emergency requests is vital to save lives when a disaster occurs. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a kind of metaheuristic algorithms applied to deal with the problem of scheduling. This paper analyzed the motion pattern of particles in a square potential well, given the position equation of the particles by solving the Schrödinger equation and proposed the Binary Correlation QPSO Algorithm Based on Square Potential Well (BCQSPSO). In this novel algorithm, the intrinsic cognitive link between particles’ experience information and group sharing information was created by using normal Copula function. After that, the control parameters chosen strategy gives through experiments. Finally, the simulation results of the test functions show that the improved algorithms outperform the original QPSO algorithm and due to the error gradient information will not be over utilized in square potential well, the particles are easy to jump out of the local optimum, the BCQSPSO is more suitable to solve the functions with correlative variables.     

Cultural algorithm-based quantum-behaved particle swarm optimization

A hybrid quantum-behaved particle swarm optimization (QPSO) based on cultural algorithm (CA), which we call cultural QPSO, is proposed. Although QPSO is a promising algorithm for many optimization problems, it is apt to lose the diversity of the swarm in the later period of the search and prematurely converges to the local optimum. Inspired by the structure of human society, this paper uses the CA model to diversify the QPSO population and improve the QPSO's performance. In this model, the swarm is divided into two sub-swarms: the common particle and the elite particle sub-swarm. If a particle comes from a common sub-swarm, it will evolve according to the QPSO method, and during the evolvement, it will be affected not only by the other common particles but also by the elites. For the elites, the differential evolution (DE) method is adopted for evolvement. After each generation, the elites will be re-elected from the whole swarm according to fitness values. The simulation results on benchmark functions demonstrate that cultural QPSO outperforms the original QPSO for many problems.     

An improved quantum-behaved particle swarm optimization and its application to medical image registration

This paper investigates the quantum-behaved particle swarm optimization (QPSO) algorithm from the perspective of estimation of distribution algorithm (EDA) which reveals the reason of QPSO's superiority. A revised QPSO (RQPSO) technique with a novel iterative equation is also proposed. The modified technique is deduced from the distribution function of the sum of two random variables with exponential and normal distribution, respectively. We present a diversity-controlled RQPSO (DRQPSO) algorithm, which helps prevent the evolutionary algorithms’ tendency to be easily trapped into local optima as a result of rapid decline in diversity. Both the RQPSO and DRQPSO are tested on three benchmark functions, as well as in medical image registration for performance comparison with the particle swarm optimization and QPSO.     

基于多种群量子粒子群优化的属性约简

现代工业发展要求迅速、可靠地实现故障诊断。针对粒子群约简算法易陷入局部最优等问题,提出了一种多种群量子粒子群优化算法（MIQPSO）。该算法对量子粒子群算法进行分群,并通过接种疫苗,指导粒子朝更优化方向进化,提高了量子粒子群的收敛速度和寻优能力。利用UCI相关数据集,通过对Hu算法、粒子群算法、量子粒子群算法、多种群量子粒子群算法的粗糙集属性约简验证,结果表明,基于多种群量子粒子群优化的约简算法具有良好的约简效果。     

基于QPSO算法训练SVM*

针对群体智能和约束优化问题的特点,提出了将QPSO算法应用于求解约束优化问题,证明QPSO算法在SVM领域中具有很高的应用价值,并为解决大规模的QP问题开辟了一条新的途径.     

基于QPSO—RBF NN的混沌时间序列预测*

提出一种基于量子粒子群优化算法训练径向基函数神经网络进行混沌时间序列预测的新方法.在确定径向基函数网络的隐层节点数后,将相应网络的参数,包括隐层基函数中心、扩展常数,以及输出权值和偏移编码成学习算法中的粒子个体,在全局空间中搜索具有最优适应值的参数向量.实例仿真证实了该方法的有效性.