Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

Fuzzy neural and chaotic searching hybrid algorithm and its application in electric customers’s credit risk evaluation

To evaluate the credit risk of customers in power market precisely, the new chaotic searching and fuzzy neural network (FNN) hybrid algorithm were proposed. By combining with the chaotic searching, the learning ability of the FNN was markedly enhanced. Customers’ actual credit flaw data of power supply enterprises were collected to carry on the real evaluation, which can be treated as example for the model. The result shows that the proposed method surpasses the traditional statistical models in regard to the precision of forecasting and has a practical value. Compared with the results of ordinary FNN and ANN, the precision of the proposed algorithm can be enhanced by 2.2% and 4.5%, respectively.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search.In the CDEPSO algorithm,DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO,while the best solution in the PSO is performed by chaotic local search.To investigate the performance of CDEPSO,four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search).And,compared with the other six improved meta-heuristics,CDEPSO also exhibits more robust performance.In addition,a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics,and CDEPSO algorithm is the best in solving these problems.

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.

基于CSSA-BPNN模型的胶结充填体动态抗压强度预测

充填采矿法二步骤回采时胶结充填体稳定性受爆破扰动而降低。为快速准确地获得充填体动态抗压强度,利用分离式霍普金森压杆(SHPB)进行了40组不同应变率的单轴冲击实验,以灰砂比、充填体密度、养护龄期和平均应变率作为输入参数,充填体动态抗压强度作为输出参数,建立了一种基于Logistic混沌麻雀搜索算法(CSSA)优化BP神经网络(BPNN)的预测模型,并与传统BPNN和麻雀搜索算法优化的BPNN进行了对比分析。结果表明:CSSA-BPNN模型的平均相对误差为4.11%,预测值与实测值之间拟合的相关系数均在0.96以上,模型预测精度高。CSSA-BPNN模型的均方根误差为0.395 0 MPa,平均绝对误差为0.359 2 MPa,决定系数为0.995 2,均优于另外两种预测模型。实现了对充填体动态抗压强度的准确预测,可大幅减小物理实验量,为矿山胶结充填体的强度设计提供了一种新方法。

一种改进的变权科莫多优化算法及其应用

针对科莫多算法(KMA)在求解复杂函数和高维情况下容易出现早熟收敛的问题,提出了一种改进的变权科莫多优化算法(VWCKMA)。首先利用Tent混沌映射产生的序列对科莫多个体位置进行位置初始化,为全局搜索的多样性奠定基础。然后提出可变惯性权重,分别对不同社会等级的科莫多个体的运动进行不同控制,较好地提高了收敛速度。最后利用Tent混沌映射进行局部扰动,使其能够进行更加精确的局部搜索,避免局部最优值。仿真实验表明,在单峰函数和多峰函数求解的标准差和均值中,VWCKMA在收敛精度和收敛速度方面均有很大的提高。针对实际空气污染物PM_(2.5)预测非线性的问题,利用VWCKMA对BP神经网络的权值和阈值进行迭代寻优,基于最优参数的条件下使用BP神经网络对PM_(2.5)进行预测。实验结果表明预测准确率为85.085%,相比单一BP神经网络预测准确率提高19.85个百分点,体现VWCKMA具有一定的实践应用价值。

基于多策略麻雀搜索算法的机器人路径规划

通过多种策略对基本麻雀搜索算法(SSA)进行改进,以解决麻雀搜索算法后期由于种群多样性丢失而导致的全局优化精度和速度问题。首先,改进无限折叠迭代映射(ICMIC)初始化种群,将自适应分段步长因子引入麻雀探测器的位置更新公式中,使麻雀搜索算法观察者的固定比例系数随迭代次数动态变化。然后,将观察者的位置与新公式和正弦余弦算法(SCA)相结合,并干扰先前的观察者步长。最后,在基准测试函数上比较了改进的麻雀搜索算法(ISSA)、麻雀搜索算法(SSA)、鲸鱼算法(WOA)、灰狼算法(GWO)、改进的灰狼算法(CGWO)、正弦余弦算法(SCA)和粒子群优化算法(PSO)的收敛性和准确性,并将其应用于路径规划。实验表明改进的麻雀搜索算法具有良好的优化性能。

基于改进的SSA-BP神经网络的矿井突水水源识别模型研究

机器学习与寻优算法的结合在矿井突水水源识别上得到广泛应用,但突水水样数据具有随机性且寻优算法易陷入局部最优,提高模型泛化能力和跳出局部最优需进一步研究。针对上述问题,提出了一种改进的麻雀搜索算法(SSA)优化BP神经网络模型,用于对矿井突水水源进行定量辨识。以鲁能煤电股份有限公司阳城煤矿为研究对象,通过常规离子浓度分析、Piper三线图对该煤矿水样的水化学特征进行分析,初步判断矿井水来源于奥灰含水层和三灰含水层,并确定Na^(+)+K^(+)浓度、Ca^(2+)浓度、Mg^(2+)浓度、HCO_(3)^(-)浓度、SO_(4)^(2-)浓度、Cl^(-)浓度、矿化度、总硬度、pH值作为突水水源识别指标;建立基于改进SSA-BP神经网络的矿井突水水源识别模型:首先进行SSA参数设置,引入Sine混沌映射使麻雀种群均匀分布,然后通过计算适应度值进行麻雀种群的更新,引入随机游走策略扰动当前最优个体,如果满足终止条件,则获得最优BP神经网络权重和阈值,最后基于构建的BP神经网络,输出识别结果。研究结果表明:①改进的SSA-BP模型在训练集上的识别准确率达95.6%,在测试集上的识别准确率达100%。②改进的SSA-BP神经网络模型与BP神经网络模型、SSA-BP神经网络模型对比结果:BP神经网络模型误判率为5/18,SSA-BP神经网络模型的误判率为2/18,改进的SSA-BP神经网络模型误判率为0,迭代10次后趋于稳定,且与设定的目标误差相差最小,初始适应度值最优,识别结果可信度高。③将阳城煤矿5组矿井水水样数据作为输入层数据输入到训练好的模型中,矿井水水样的主要来源为奥灰含水层、三灰含水层和山西组含水层,模型识别结果与水化学特征分析的结论相互印证,实现了精准区分。

混合策略改进的野马优化算法

针对野马优化算法存在种群多样性低、收敛速度慢和易陷入局部最优等问题,提出一种混合策略改进的野马优化算法(IWHO)。在马驹位置公式中引入基于饥饿游戏的Tent惯性权重,更好平衡算法的全局搜索与局部搜索能力;在放牧阶段引入折射镜像学习策略,利用折射镜像学习生成可行解的反向解,加快算法的求解速度;利用混合黄金正弦与飞蛾扑火算子,使算法跳出局部最优。将改进后的算法(IWHO)和其它算法在10个基准函数上对比测试,并通过Wilcoxon秩和检验和拉/压弹簧设计问题验证算法性能。仿真结果表明,IWHO在收敛速度和寻优精度上有明显改进。

加入淘汰机制的改进麻雀搜索算法

传统麻雀算法(sparrow search algorithm,SSA)具有搜索精度高、寻优能力强等优点,但同时也存在早熟收敛、迭代过程中容易陷入局部最优值等问题。针对这些问题,提出了一种加入Tent混沌映射和末位淘汰机制的麻雀搜索算法(sparrow search algorithm with tent,TESSA)。采用2N分段Tent混沌映射初始化种群位置。同时在算法迭代后期引入非线性末位淘汰机制,提高其收敛速度和精度。经过与其他4种群智能算法在6个基准函数上求解性能相比,TESSA的收敛速度、寻优精度、标准误差等性能指标有明显的优势。

融入动态学习与高斯变异的自适应秃鹰搜索算法

针对标准秃鹰搜索算法寻优时存在的初始种群分布不均匀、个体适应性差和易陷入局部最优等问题,提出一种改进的秃鹰搜索算法应用于求解函数优化问题。首先,引入Circle混沌映射丰富初始种群的多样性,在搜索空间阶段引入一种非线性权重自适应地调整算法搜索与开发的能力;其次,令螺旋搜索过程中秃鹰领导者个体向具有代表性的秃鹰个体进行动态学习,产生出适应性强的秃鹰个体;最后,对最佳搜索位置的秃鹰个体执行高斯变异策略,根据个体适应度大小择优更新曲线俯冲过程中的秃鹰领导者个体,种群中多数秃鹰个体的适应性得到增强,能够一定程度上避免算法在函数寻优时出现的停滞局面。通过在若干基准测试函数与部分CEC2017函数的对比实验验证了本文算法的优越性。

全粒子推动野马优化算法的无人机三维路径规划

针对无人机路径规划求解计算量大、难收敛等问题,提出了一种基于全粒子推动野马算法的路径规划方法。建立三维环境模型与路径代价模型,将路径规划问题转化为多维函数优化问题;采用一种自适应邻域搜索策略,改善算法的开发能力;利用高斯随机游走策略对个体的历史最优位置进行回溯搜索,改善算法的探索能力;考虑到自适应策略对初始种群多样性敏感的问题,结合Tent混沌映射初始化种群,提高算法的鲁棒性以及全局寻优能力;将提出的改进算法在13个经典测试函数中进行性能验证,并移植于无人机三维路径规划问题中。在30峰、40峰、50峰的环境模型下进行测试,与遗传算法、粒子群算法、SRM-PSO(self-regulating and self-perception particle swarm optimization with mutation mechanism)算法以及野马算法对比,全粒子推动野马算法皆取得最短平均路径,且在所有测试中都找到满足约束、无碰的路径。仿真结果证明,在复杂环境下全粒子推动野马算法具有优秀的全局寻优能力以及较好的鲁棒性。

基于改进TSO优化Xception的PEMFC故障诊断

针对质子交换膜燃料电池(PEMFC)的故障诊断问题,提出了一种利用改进的瞬态搜索优化(TSO)算法优化Xception网络的故障通用诊断方法。首先,对故障数据进行线性判别分析降维和归一化处理,在保留主要特征的前提下降低计算复杂度;其次,引入Tent混沌映射和反向学习策略增强TSO算法的全局搜索能力,在训练阶段对Xception神经网络的超参数进行优化;最后,使用充分训练的Xception网络对PEMFC故障进行分类识别,并与经典的分类模型进行对比。在基于实验测量的水管理故障数据和仿真产生的多类故障数据上,Xception均取得了最高的分类准确率,分别为100%和98.08%,这表明Xception对数据特征的提取能力较强,且所提方法能作为一种PEMFC故障的通用诊断方法。

混合多项自适应权重的混沌麻雀搜索算法

麻雀搜索算法具有原理简单、搜索能力强、快速寻优等优点,但是存在全局搜索不足、易陷入局部最优等缺点,针对其缺点提出了混合多项自适应权重的混沌麻雀搜索算法。增加改进Circle混沌映射提高种群多样性;在发现者引入自适应权重策略,提高发现者的全局搜索能力和搜索范围;在加入者引入改进鲸鱼优化算法的气泡网捕食策略,提高算法的局部搜索性能和跳出局部最优的能力;结合反向学习策略机制,对所有的个体进行最优选择,使每次迭代后的个体质量得到提升,以提高算法的寻优效率和寻优精度。将混合多项自适应权重的混沌麻雀搜索算法与4种经典基本算法和9种改进的麻雀搜索算法在12种测试函数和CEC2022测试函数上进行对比,改进算法有更好的寻优性能和收敛速度。

基于PWLCM和秃鹰俯冲机制改进的野狗优化算法

针对野狗优化算法(Dingo Optimization Algorithm,DOA)收敛速度偏慢和寻优精度较低等问题,提出一种基于PWLCM和秃鹰机制改进的野狗优化算法(Improved Dingo Optimization Algorithm,IDOA)。首先,使用具有遍历性的分段线性混沌映射(Piecewise Linear Chaotic Map,PWLCM)初始化野狗种群,有效增加野狗种群多样性。其次,在迫害策略中引入秃鹰俯冲机制,加快野狗捕获猎物的速度,加强算法探索局部的能力。最后,在食腐策略引入螺旋搜索因子,增强算法的局部寻优能力,提升算法的寻优速度和求解精度。仿真实验数据、消融实验以及Wilcoxon秩和检验均表明,与其他对比算法相比,提出的IDOA在所有测试函数上有着更佳的寻优速度以及寻优精度;与其他改进的野狗优化算法相比,所提出的IDOA展现出更好的整体性能。

基于改进秃鹰算法优化极限学习机的谐波发射水平估计

针对目前电力系统谐波发射水平难以直接测量的问题,提出了一种基于改进秃鹰算法(improved bald eagle search,IBES)优化极限学习机(extreme learning machine,ELM)的谐波发射水平估计方法。首先,在传统秃鹰搜索算法中引入Tent混沌映射和柯西变异算子,利用IBES算法对ELM模型的输入权重和阈值进行寻优。其次,输入公共连接点(point of common coupling,PCC)处谐波电压和谐波电流,代入IBES-ELM模型,估计用户侧和系统侧谐波发射水平。最后进行仿真和工程实例分析,并与其他算法的估计结果进行对比。结果表明,所提IBES-ELM方法估计精度优于长短期记忆网络(long short-term memory,LSTM)、卷积神经网络(convolution neural network,CNN)、反向传播神经网络(back propagation neural network,BP)和CNN-LSTM算法模型,验证了该方法的有效性和稳定性。

嵌入翻筋斗策略的自适应秃鹰搜索算法及其应用

针对秃鹰搜索算法(BES)容易陷入局部最优与求解精度低等问题,本文提出一种改进的秃鹰搜索算法。首先,通过Circle混沌序列取代原始算法中随机产生的初始种群,提升了初始种群的多样性;其次,在算法搜索选择空间阶段中,结合自适应权重对秃鹰个体位置进行更新,平衡算法的搜索与开发能力;最后,利用翻筋斗觅食策略更新后续阶段秃鹰领导者个体位置,并融入精英差分变异增强算法跳出局部最优的能力。在多个标准测试函数进行对比仿真实验,并应用改进算法对随机森林分类参数进行优化,实验结果表明,改进后的算法在求解效率方面有较大提升,且求解精度与分类准确率也优于传统算法。

基于改进麻雀搜索算法的多维复杂函数优化问题的求解

传统麻雀搜索算法在寻找最优解过程中,存在种群多样性不够高、易产生局部最优、收敛精度不稳定等问题.本文给出了一种改进的麻雀搜索算法,首先,用改进的Cubic和Bernoulli混合混沌映射初始化种群分布提高种群多样性;其次,在算法迭代过程中引入非线性自适应惯性权重和Levy飞行策略,调节算法的搜索范围和精度,改善算法的收敛速度和局部寻优能力;然后,引入鲸鱼优化算法的捕食策略进行扰动,避免陷入局部最优;最后,在12个基准测试函数上与传统麻雀搜索算法和其他算法进行评估,实验结果验证了改进的算法具有较好的收敛速度和求解精度,并提升了局部搜索能力.