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.

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.

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.

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。

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。

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。

融合混沌映射和乘除算子的花授粉算法及应用

花授粉算法是一种群智能算法,广泛应用于各个领域。本文针对该算法存在收敛精度低、收敛速度慢、稳定性差等不足,提出基于混沌映射和乘除算子的花授粉算法(MDFPA)。首先,利用混合混沌映射更好的随机分布能力生成初始种群,减小随机误差给算法带来的影响;其次,反双曲正切函数有良好的性能,将其引入动态转换概率中,替换固定转换概率,提高算法收敛能力,有利于控制全局搜索和局部搜索之间的平衡;最后,在全局搜索阶段引入乘除算子对花粉位置进行放缩,在空间里进行充分搜索,避免算法陷入局部最优。通过对12个测试函数的仿真实验,表明该算法在单峰、多峰和固定维度测试函数上性能都优于花授粉算法(FPA)、粒子群算法(PSO)、布谷鸟算法(CS)和蜂群算法(ABC),寻优性能显著性提高,具有更快的收敛速度和更优的求解精度。将MDFPA算法应用于PID(Proportion Integration Differentiation)控制器参数优化中,结果表明优化后的PID控制器性能更优。

基于动态双种群的黏菌和花粉混合算法

针对单一启发式算法易受自身原理导致的全局和局部搜索不平衡的问题,提出了一种基于动态双种群的黏菌和花粉混合算法HASMFP。首先,通过种群个体和当前最优个体之间的距离,将种群动态划分为黏菌子种群和花粉子种群分别进行搜索,以更有效地平衡算法的探索能力和开发能力;其次,对全局搜索融入相似度与适应度的综合排序感知机制来提高黏菌子种群的多样性,以帮助黏菌算法跳出局部最优;最后,在标准花粉算法的全局搜索中加入动态权重和恒定收缩系数,并对局部搜索设计了精英引导项来提高算法的收敛速度和搜索精度。选用CEC2017测试集中的12个函数作为实验测试集,将HASMFP与ISMA、DTSMA、HLFPA、SCFPA和tMFPA五种改进算法进行性能测试对比。还对HASMFP的各个改进策略进行消融实验,实验表明在综合改进策略的共同作用下,HASMFP的优化性能排名第一。基于实验结果的Friedman检验表明,HASMFP能够获取最优的性能。

面向阶段任务的携行器材品种确定方法

维修器材是有效实施维修保障的物质基础,携行器材品种确定是开展维修器材携行决策的关键。针对执行阶段任务武器装备维修器材品种多、影响因素复杂且关联关系不明确造成的携行器材品种确定困难的现实问题,提出了一种将改进稀疏核主成分分析(sparse kernel principal component analysis,SKPCA)算法与长短时记忆(long short-term memory,LSTM)神经网络模型相结合的阶段任务携行器材品种确定方法。在分析与任务阶段时序相关的携行器材影响因素及特征指标的基础上,运用基于弹性惩罚的SKPCA降维算法,对器材特征进行降维分析并得到低维稀疏特征向量,通过缩减数据容量增强特征指标的可解释性;运用混沌序列改进花授粉算法(flower pollination algorithm,FPA)优化LSTM超参数,构建混沌FPA-LSTM预测模型,精准进行携行器材品种确定。通过对演习携行器材品种确定算例分析验证了所提方法的科学性和可行性。

基于改进数字高程模型的架空线路杆塔基础土方量计算方法研究

架空输电线路建设工程中,杆塔基础土方量计算影响着工程经济效益和施工工期,因此,需要选取合适的土方量计算方法。本文中首先分析了断面法、方格网法、等高线法和数字高程模型(DigitalElevationModel,DEM)法4种方法,并采用花授粉算法(FlowerPollinationAlgorithm,FPA)对传统DEM方法进行改进。将5种不同的方法运用到实际案例中比较不同方法的适应地形及计算精度。结果表明,改进后的DEM法可以获得X、Y、Z轴上的最优分割网格数量,适用地形范围最广、计算精度最高,计算速度相较于传统DEM法也有提高。

基于离散花授粉算法的分布式新能源继电保护整定优化方法

现有继电保护整定优化方法需要进行多次试验和调整,耗费大量时间和资源,对于大规模分布式新能源系统是非常不可行的。利用离散花授粉算法,对分布式新能源继电保护整定优化进行研究。以时间整定系数和启动电流为优化参数,构建了目标函数。采用离散花授粉算法对其进行优化求解。实验结果表明:设计方法在不到15次迭代下即可获得最优解,且耗时始终保持在60s之内,能够快速且准确的计算出继电保护的整定结果。

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy.Photovoltaic arrays are used to harvest solar energy.The major goal,in harvesting the maximum possible power,is to operate the system at its maximum power point(MPP).If the irradiation conditions are uniform,the P-V curve of the PV array has only one peak that is called its MPP.But when the irradiation conditions are non-uniform,the P-V curve has multiple peaks.Each peak represents an MPP for a specific irradiation condition.The highest of all the peaks is called Global Maximum Power Point(GMPP).Under uniform irradiation conditions,there is zero or no partial shading.But the changing irradiance causes a shading effect which is called Partial Shading.Many conventional and soft computing techniques have been in use to harvest solar energy.These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong.In this paper,a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning(OBL)to deal with partial shading conditions.Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.