Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6(IPv6)is the latest version of IP that goal to host 3.4×10^(38)unique IP addresses of devices in the network.IPv6 has introduced new features like Neighbour Discovery Protocol(NDP)and Address Auto-configuration Scheme.IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol(ICMPv6).IPv6 is vulnerable to numerous attacks like Denial of Service(DoS)and Distributed Denial of Service(DDoS)which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications.Therefore,an Intrusion Detection System(IDS)is a monitoring system of the security of a network that detects suspicious activities and deals with amassive amount of data comprised of repetitive and inappropriate features which affect the detection rate.A feature selection(FS)technique helps to reduce the computation time and complexity by selecting the optimum subset of features.This paper proposes a method for detecting DDoS flooding attacks(FA)based on ICMPv6 messages using a Binary Flower PollinationAlgorithm(BFPA-FA).The proposed method(BFPA-FA)employs FS technology with a support vector machine(SVM)to identify the most relevant,influential features.Moreover,The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios.The results show that the proposed method BFPAFA achieved the best accuracy rate(97.96%)for the ICMPv6 DDoS detection with a reduced number of features(9)to half the total(19)features.The proven proposed method BFPA-FAis effective in the ICMPv6 DDoS attacks via IDS.

A New Flower Pollination Algorithm Strategy for MPPT of Partially Shaded Photovoltaic Arrays

Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.

求解带时间窗车辆路径问题的改进FPA

车辆路径规划问题广泛应用于物流行业,为解决这一NP难的组合优化问题,提出一种求解带时间窗车辆路径问题的改进花授粉算法。针对FPA存在寻优精度低和过早陷入局部最优等缺陷,在原始FPA中引入遗传算法的交叉和变异因子,设计基于精英父代的多点交叉算子和单亲多点基因变异换位算子;对FPA中的转换概率p进行自适应调整并重新定义全局授粉和局部授粉操作;采用国际通用标准测试集Solomon对算法进行测试,将求得结果与已知多个算法求得的结果进行对比分析。其结果表明,改进FPA求解带时间窗车辆路径问题是可行有效的。

A Novel Flower Pollination Algorithm to Solve Load Frequency Control for a Hydro-Thermal Deregulated Power System

Load frequency control plays a vital role in power system operation and control. LFC regulates the frequency of larger interconnected power systems and keeps the net interchange of power between the pool members at predetermined values for the corresponding changes in load demand. In this paper, the two-area, hydrothermal deregulated power system is considered with Redox Flow Batteries (RFB) in both the areas. RFB is an energy storage device, which converts electrical energy into chemical energy, that is used to meet the sudden requirement of real power load and hence very effective in reducing the peak shoots. With conventional proportional-integral (PI) controller, it is difficult to get the optimum solution. Hence, intelligent techniques are used to tune the PI controller of the LFC to improve the dynamic response. In the family of intelligent techniques, a recent nature inspired algorithm called the Flower Pollination Algorithm (FPA) gives the global minima solution. The optimal value of the controller is determined by minimizing the ISE. The results show that the proposed FPA tuned PI controller improves the dynamic response of the deregulated system faster than the PI controller for different cases. The simulation is implemented in MATLAB environment.

Adaptive Flower Pollination Algorithm Based on Chaotic Map

Flower pollination algorithm (FPA) is one of the well-known evolutionary techniques used extensively to solve optimization problems. Despite its efficiency and wide use, the identical search behaviors may lead the algorithm to converge to local optima. In this paper, an adaptive FPA based on chaotic map (CAFPA) is proposed. The proposed algorithm first used the ergodicity of the logistic chaos mechanism, and chaotic mapping of the initial population to make the initial iterative population more evenly distributed in the solution space. Then at the self-pollination stage, the over-random condition of the gamete renewal was improved, the traction force of contemporary optimal position was given, and adaptive logarithmic inertia weight was introduced to adjust the proportion between the contemporary pollen position and disturbance to improve the performance of the algorithm. By comparing the new algorithm with three famous optimization algorithms, the accuracy and performance of the proposed approach are evaluated by 14 well-known benchmark functions. Statistical comparisons of experimental results show that CAFPA is superior to FPA, PSO, and BOA in terms of convergence speed and robustness.

基于BASFPA-BP的可靠性预测模型研究

软件可靠性预测以软件可靠性预测模型为基础,对软件的可靠性以及与其直接相关的度量进行分析、评价和预测,利用软件运行中所收集的失效数据对未来的软件可靠性进行预测,成为了评估软件失效行为和保障软件可靠程度的重要手段。BP神经网络结构简单、参数少、易实现,在软件可靠性预测领域已经得到了广泛应用。然而基于传统BP神经网络搭建的软件可靠性预测模型的预测精度无法达到预期目标,因此提出了基于BASFPA-BP的软件可靠性预测模型。该模型利用软件失效数据,在BP神经网络训练过程中利用BASFPA算法优化网络权值、阈值,从而提高模型的预测精度。选用3组公开的软件失效数据,将实际值与预测值的均方误差作为预测结果的衡量标准,同时将BASFPA-BP与FPA-BP,BP,Elman这3种模型进行对比研究。实验结果表明,基于BASFPA-BP的软件可靠性预测模型在同类型模型中实现了较高的预测精度。

Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

Flower Pollination Heuristics for Nonlinear Active Noise Control Systems

In this paper,a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems.The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal,random and complex random signals as noise interferences.The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series.The comparative study on statistical observations in terms of accuracy,convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable,accurate,stable as well as robust for active noise control system.The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms,particle swarm optimization,backtracking search optimization algorithm,fireworks optimization algorithm along with their memetic combination with local search methodologies.Moreover,the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.

Flower Pollination Heuristics for Parameter Estimation of Electromagnetic Plane Waves

For the last few decades,the parameter estimation of electromagnetic plane waves i.e.,far field sources,impinging on antenna array geometries has attracted a lot of researchers due to their use in radar,sonar and under water acoustic environments.In this work,nature inspired heuristics based on the flower pollination algorithm(FPA)is designed for the estimation problem of amplitude and direction of arrival of far field sources impinging on uniform linear array(ULA).Using the approximation in mean squared error sense,a fitness function of the problem is developed and the strength of the FPA is utilized for optimization of the cost function representing scenarios for various number of sources non-coherent located in the far field.The worth of the proposed FPA based nature inspired computing heuristic is established through assessment studies on fitness,histograms,cumulative distribution function and box plots analysis.The other worthy perks of the proposed scheme include simplicity of concept,ease in the implementation,extendibility and wide range of applicability to solve complex optimization problems.These salient features make the proposed approach as an attractive alternative to be exploited for solving different parameter estimation problems arising in nonlinear systems,power signal modelling,image processing and fault diagnosis.

基于FPA算法的金属氧化物避雷器在线监测

为了有效解决金属氧化物避雷器(MOA)在线监测难、监测误差较大等问题,通过引入群智能算法中的花朵授粉算法(Flower Pollination Algorithm,FPA),利用花朵授粉算法具有良好的寻优效果来达到需要监测的效果,降低人工监测的失误率,提高监测效率。根据系统运行时的实际电压和泄露电流进行在线的实时监测,并根据这两个参数对MOA等效模型中的参数进行求解,得到能够正常反映系统工况的监测数值。同时,利用MATLAB仿真软件,对电网中的谐波电压和频率进行实时仿真,观测此算法的引入是否能提高监测的准确性,通过计算得到,具有寻优能力的FPA算法,能够将标准差趋近于真实的泄露电流,并且能够有效降低相关系数的最大误差,可得;该算法对此模型中的谐波电压和频率影响较小,同时,具有降低监测误差的能力。

基于神经网络优化的花朵授粉算法

为了降低花朵授粉算法(FPA)重复探索的情况,并提高算法的种群多样性和空间搜索能力,提出一种基于神经网络优化的花朵授粉算法(NNFPA)。设定自适应控制因子,从而动态地切换全局与局部搜索;利用多方信息的全局搜索策略提高算法收敛速度并维持花粉种群的多样性,同时减少在算法迭代后期种群对社会属性的依赖;基于神经网络的局部搜索策略让算法具有记忆功能,这样算法就能具有稳定搜索策略,从而降低算法的不确定性,使它能更充分地探索解空间。选取9个常规测试函数与CEC2014测试集中的部分函数进行仿真实验,得到的结果表明:与标准FPA以及变种算法HSFPA(FPA based on Hybrid Strategy)相比,NNFPA在所选测试函数上具有较高的搜索精度和收敛速度。可见NNFPA具有更好的寻优能力。

多策略混合的花朵授粉算法

针对花朵授粉算法(FPA)在解决高维度问题时存在收敛速度慢和收敛精度低的问题,本文提出一种混合了多种策略的花朵授粉算法(MFPA).该算法通过使用自适应控制因子来动态地切换全局与局部搜索策略的使用;为了提高收敛速度和维持花粉种群多样性,提出一种基于多方信息的全局搜索策略;为了能探索到更充分的解空间,提出一种局部搜索策略;为提高算法的搜索解空间能力,引入特征选择策略降低问题复杂度.基于多种类型测试函数开展模拟实验,与多种优秀算法进行算法性能对比分析,实验结论:MFPA算法在收敛速度与精度方面有着更好的表现,适用于求解大规模复杂优化问题.

基于改进FPA算法的配电网光伏消纳能力评估

配电网中接入分布式光伏电源会带来诸多不利影响,研究配电网分布式光伏的消纳能力问题对维持配电网的安全运行至关重要。文章研究了分布式光伏电源接入对配电网电能质量、短路容量及网损的影响,以分布式光伏电源接入量最大、网损最小为目标函数,以电压偏差、电压波动及短路容量为约束条件,建立了分布式光伏消纳能力评估模型。根据所建模型特点,提出了一种改进的花授粉优化算法(Flower Pollination Algorithm,FPA),并对IEEE33节点系统及实际配电网进行求解。最后验证了所建模型的正确性及算法的有效性。

基于量子行为花朵授粉算法优化LSTM模型

针对传统花朵授粉算法(flower pollination algorithm, FPA)受初始参数影响较大、且易陷入局部最优解或算法无法收敛等问题,提出一种基于量子行为的花朵授粉算法(quantum-inspired flower pollination algorithm, QFPA).通过引入量子系统到FPA中,使授粉过程中的搜索更高效,从而提高全局搜索能力.此外,还引入轨迹分析,使种群能更好地逃离局部最优解,进一步降低误差.为验证该方法的有效性,先通过选定的几个基准函数对QFPA进行评估,然后采用评估效果最好的QFPA对长短期记忆网络(LSTM)模型超参数进行寻优,最后在用自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)算法去除噪声后的空气质量数据集上进行实验,并与其他几种常用的优化算法进行对比.实验结果表明:QFPA提高了优化算法的全局搜索能力和收敛性;QFPA-LSTM模型增强了长时间序列数据预测的准确性和效率,该模型预测的均方根误差为10.93μg/m^(3),为实际应用中的空气质量预测提供了可靠的解决方案.

基于深度学习的室内可见光通信光源配置策略

为了在不同的室内环境下寻求最优的光源优化方案,提出了一种基于深度学习的室内可见光通信光源配置策略。分别引入室内障碍物、自然光和人员移动干扰,以室内信噪比均匀度为目标函数,使用花授粉算法(FPA)对不同的房间状态下的光源功率和半功率角进行优化;将得到的房间状态和光源配置作为训练集,使用卷积神经网络(CNN)进行训练,得到的计算模型可以在不同房间状态下对最优光源设置进行预测,实现光源配置的动态调整。仿真结果表明:该策略的光源参数合格率达88%,且预测结果在房间信号功率和光照强度上均满足要求。

不完美维护下质量退化系统生产维护联合优化

针对设备劣化串联生产系统中质量退化问题,基于维纳过程对设备劣化建模,考虑设备不完美维护对残余劣化量和残余劣化率的综合影响,提出基于实时产品质量信息的设备不完美维护与换新策略,并与生产计划联合优化,建立最小化总成本的数学模型。利用蒙特卡洛仿真对总成本抽样,采用优化转换概率的花粉授粉算法对模型求解。数值实验证明,利用实时质量信息的视情维护策略相较于3类对比策略总成本更低,在高生产负荷系统下平均优化比例为9.61%,在低生产负荷系统下平均优化比例为10.71%。

充分搜索多策略花授粉算法在PID参数优化中的应用

PID控制器广泛应用于工业领域,结构简单,控制效果良好,参数对控制器起到了绝对作用。花授粉算法是一种应用广泛的元启发算法,但存在易陷入局部最优、迭代后期收敛速度慢、寻优精度差等不足。用加入随机扰动的反双曲正切函数充分搜索策略的转换概率替换原本的固定概率,平衡全局搜索和局部搜索;在异花授粉中引入新型动态因子,改变母系花粉位置的影响;在自花授粉阶段提出权重学习策略,让花粉向优秀花粉方向聚拢;引入翻筋斗探索策略,加大种群多样性。在此基础上提出充分搜索下多策略花授粉算法(MSFPA),对比分析了花授粉算法(FPA)、粒子群算法(PSO)、差分进化算法(DE)和象群算法(EHO)对9个测试函数的仿真实验,结果表明MSFPA算法收敛速度快,性能更优。将MSFPA算法应用于PID参数优化中,经过优化的系统超调量较小,且调整时间较短,有较强的稳定性。

基于改进花授粉算法的永磁同步电机参数辨识

针对传统花授粉算法辨识永磁同步电机参数迭代后期易陷入局部最优导致收敛速度慢和寻优精度低的缺陷,提出了一种基于t-分布扰动和高斯扰动的改进花授粉算法(tGFPA),以实现永磁同步电机参数的高精度辨识。首先利用混沌Logistic映射对花朵个体位置进行初始化,然后在全局授粉过程中引入t-分布扰动,提高搜索空间的多样性。在局部授粉过程中加入高斯扰动,增强跳出局部最优解的能力。最后,对比仿真结果表明:基于双扰动策略的改进花授粉算法收敛速度更快、辨识精度更高,对于永磁同步电机控制性能改善具有重要意义。

基于FPA-VMD和BiLSTM神经网络的新型两阶段短期电力负荷预测

短期电力负荷预测有助于维持发电端和用电端的动态平衡,保障电力系统稳定且高效地运行。分布式能源的大规模并网以及气象和节假日等短期因素的影响,使得负荷序列呈现明显的波动性和非线性。为此,该文提出基于花授粉算法(flower pollination algorithm,FPA)优化变分模态分解(variational mode decomposition,VMD)和双向长短时记忆(bidirectional long and short time memory,BiLSTM)神经网络的新型两阶段短期电力负荷预测方法。第一阶段首先提出了一种关于分解损失的VMD评价标准,并采用FPA来寻找该标准下分解参数的最优组合,从而降低了经验设置参数的随机性并且减少了分解过程中的信号损失,提高了分解质量;其次针对分解所得的每个子序列分别建立具备双向处理和长期记忆的BiLSTM神经网络,从而可以更好地挖掘负荷数据的过去和未来的深度时序特征。第二阶段综合考虑模态分量以及气象和星期类型等短期因素的影响,建立基于BiLSTM神经网络的误差纠正模型,用以挖掘误差中所包含的隐含信息,从而降低了模型的固有误差。将该文方法应用于美国南部某地区的负荷数据集,最终的平均绝对误差(mean absolute error,MAE)、平均绝对百分比误差(mean absolute percentage error,MAPE)和均方根误差(root mean square error,RMSE)以及R2分别为108.03、1.19%、146.48以及0.9812。随后在冀北电网某供电公司的实际应用中,再次证明了该方法在区域性短期电力负荷预测中的有效性。