Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

Bacterial Foraging Algorithm based Parameter Estimation of Three WINDING Transformer

Transformers are one of the main components of any power system. An accurate estimation of system be-haviour, including load flow studies, protection, and safe control of the system calls for an accurate equiva-lent circuit parameters of all system components such as generators, transformers, etc. This paper presents a methodology to estimate the equivalent circuit parameters of the Three Winding Transformer (TWT) using Bacterial Foraging Algorithm (BFA). The estimation procedure based on load test data at one particular op-erating point namely supply voltage, load currents, input power. The performance characteristics, such as efficiency and voltage regulation are considered along with the name plate data in order to minimize the er-ror between the estimated and measured data. The estimation procedure is demonstrated with a sample three winding transformer and the results are compared against the directly measured performance of TWT and genetic algorithm optimization results. The simulation results show the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the bacte-rial foraging algorithm.

Solving Optimal Power Flow Using Modified Bacterial Foraging Algorithm Considering FACTS Devices

In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

Parameter Estimation of Single Phase Core Type Transformer Using Bacterial Foraging Algorithm

The electrical circuit equivalents of magnetic device structures such as transformer require an exact knowledge of its parameters. Efficient parameter estimation technique is essential to obtain the equivalent circuit parameters of transformer because the parameters are used to manipulate parasitic elements and to obtain the enhanced circuit performance. In this paper, Bacterial Foraging Algorithm (BFA) has been applied to estimate the equivalent circuit parameters of single phase core type transformer. The information of open Circuit (OC) and Short Circuit (SC) tests has been utilized in BFA algorithm. The effectiveness of the proposed approach has been tested with a sample transformer and the simulation results are compared against the conventional method. The numerical results show that the proposed approach outperforms the conventional method in the aspects of solution quality.

Utilization of Bacterial Foraging Algorithm for Optimization of Boost Inverter Parameters

This paper proposes a boost inverter model capable of coping with changes in load as well as line parameters. In order to achieve an output AC voltage higher than the input DC voltage, we can use this model consisting of a pair of DC-DC converters with a load connected differentially across them. This paper aims at developing a boost inverter that is capable of achieving a very high gain, to obtain an AC voltage of 110 Vrms from a DC input of 36 V. This is exceptionally beneficial in renewable energy applications, where the input voltage garnered is quite small, and in need of stepping up for commercial use or transmission. However, aside from the voltage level itself, lowering the rise time, settling time, peak overshoot and steady state error of the system is of cardinal importance in order to maintain a reliable output voltage. Closed loop control of the differentially connected DC-DC converters is necessary to determine the optimal stable operating point. This paper addresses the above concerns through optimization of the proportional and integral constants using the novel Bacterial Foraging Algorithm, ensuring operation at the required optimal stable operating point. Moreover, load/line disturbances may occur due to which the stability of output voltage may be compromised and THD value may increase to undesirable extents. In these cases, utilization of the output voltage is no longer viable for several applications sensitive to such voltage fluctuations. We have demonstrated that our proposed model is capable of restoring/reverting to the satisfactory sinusoidal waveform fashion within a single voltage cycle. The waveform results that demonstrate the resilience of our model to such disturbances are represented appropriately.

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.

基于改进蚁群算法的自动落布车路径规划

针对自动落布车在使用蚁群算法(ant colony algorithm,ACA)进行路径规划过程中出现的收敛次数多、收敛速度较慢且容易陷入局部最优的问题,提出一种改进蚁群算法(improved ant colony algorithm,IACA)。首先对信息素挥发系数ρ进行自适应调整,令其做动态变化,克服算法的收敛次数过多,加快算法收敛速度,减少算法的收敛时间;其次引入细菌觅食算法中趋化操作的趋化步长因子对信息素更新公式进行改进,削减算法迭代的后期信息素浓度值,使算法在后期跳出局部最优值,提高算法全局搜索能力。利用MATLAB将改进后的算法在3种不同的栅格环境中进行仿真验证。结果表明:相比传统蚁群算法,改进后的算法收敛次数减少81.1%,最小路径长度减少6.3%,收敛时间减少20.7%。最后搭建ROS小车实验平台,利用ROS机器人系统对改进蚁群算法在模拟的织布车间环境中进行实验验证。结果表明:对比传统蚁群算法,改进蚁群算法在寻优时间上减少了8.6%。

基于细菌觅食算法的嵌入式机械臂角度控制器设计

进行机械臂角度控制器设计过程中,为提高机器人机械臂灵活性,降低关节角度控制误差,设计一种细菌觅食算法的嵌入式机械臂角度控制器;首先,构建机械臂动力学模型以获取机械臂的柔性特征及其关节位置,根据获取的信息确定角度控制器的硬件逻辑结构和算法;然后,使用ARM微处理器嵌入式操作系统,设计包含移动控制终端和机械臂控制端的控制器硬件结构;最后,采用细菌觅食算法优化控制器参数,并实现代码完成机器人机械臂角度的精准跟踪控制;实验结果表明:所提方法的位姿跟踪精度高达99%、物体偏移距离在0~0.2 mm之间,控制运行时间在0.33~0.78 s之间,表明所提方法的角度控制误差小、稳定性强,能够保证机械臂关节角度无超调,具有极高的机器工程应用价值。

Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this algorithm.The superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.

基于新型细菌觅食优化算法的飞机动态泊位问题

随着航空运输业的发展,传统手动设计泊位方案已难以满足日益增长的外包维修需求.在外包模式下,如何快速给出高效的动态泊位方案关系到维修任务订单的准点交付,是飞机维修服务公司亟待解决的重要问题.针对飞机泊位进出顺序及碰撞检测特点,构建带时间窗的飞机维修泊位模型.设计自适应趋化学习及交叉协作策略,提出新型细菌觅食优化算法,并设计一系列约束处理机制.研究结果表明,提出的基于矩形碰撞检测方法可有效预防并判断飞机间碰撞阻塞情况.新型细菌觅食优化算法在解决飞机动态泊位问题上展现出搜索精度高、稳定性强等特点.所得高效智能化泊位调度方案有助于在保证维修安全的情况下提升飞机维修服务提供商的维修服务效率,改进维修资源利用率与维修系统的柔性,为企业实现高质量发展打下良好基础.

A novel power system reconfiguration for a distribution system with minimum load balancing index using bacterial foraging optimization algorithm

In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.

基于细菌觅食-改进蚁群优化算法的水面无人船路径规划

为了解决水面无人船全局路径规划问题,提出了一种细菌觅食-改进蚁群优化算法(bacterial foraging-improved ant colony optimization algorithm,BF-IACOA)。相较于传统蚁群优化算法(ant colony optimization algorithm,ACOA),该算法在路径搜索策略上考虑水面无人船航行需要尽可能减少转向次数和完全规避过大转向角的约束,引入转向角启发因子,综合求解转移概率;同时引入细菌觅食算法的繁殖操作和趋化操作,改进信息素浓度的更新方式,解决传统ACOA容易陷入局部最优解和收敛速度较慢的问题。仿真结果表明,相较于传统ACOA,BF-IACOA的全局搜索能力得到较大幅度的提升,并且收敛迭代次数减少超过30%;在实际水域环境模型下,BF-IACOA可以通过14次迭代为无人船规划出全局可行路径。

改进细菌觅食算法的永磁同步电机参数辨识

应用群智能算法对永磁同步电机(PMSM)进行参数辨识后期容易进入局部最优,从而导致辨识误差大,为此提出一种融合小生境技术的改进细菌觅食算法(MBFA)。通过构建目标追踪函数,利用电机电流、电压和转速等直接测量的信号实现对电机d轴电感、q轴电感、定子电阻和永磁体磁链的快速、准确辨识;辨识过程中通过引入格型准则对目标解空间进行拟蒙特卡罗采样提高算法的全局搜索能力;基于小生境技术进行在线多种群协同搜索策略提高算法的搜索效率和寻优精度;最后通过引入一种种群实时监测和动态更新机制保证了算法在整个寻优过程的鲁棒性。仿真和实验结果表明,所提算法在参数辨识的快速性、准确性、稳定性方面均表现优越,辨识结果能够满足对永磁同步电机进行建模和仿真的精度要求。

高光谱技术结合改进LSSVM的大米脂肪酸检测方法

目的:解决食品企业现有大米品质检测方法存在的准确性低和效率差等问题。方法:基于高光谱数据采集系统,提出一种结合改进细菌觅食算法和最小二乘支持向量机的贮藏大米品质快速无损检测方法。通过改进的细菌觅食算法对最小二乘支持向量机超参数(正则化参数和核参数)进行寻优,实现贮藏大米品质的快速无损检测。通过试验分析其性能。结果:所提方法可以实现贮藏大米脂肪酸含量的快速无损检测,决定系数为0.940 5,均方根误差为0.543 5,平均检测时间为1.12 s。结论:所提检测方法具有较高的检测性能,可用于大米品质的鉴别与检测。

求解路径规划问题的细菌觅食——蚁群算法

随着科技的迅速发展,蚁群算法的应用范围越来越广泛,其路径规划的速度以及路线准确度对算法要求越来越高。目前已有的蚁群算法所拥有的收敛性慢容易陷入局部最优问题,为了提高算法的准确度与速度,通过引用细菌觅食算法中的复制与趋向对其进行改进,提高此算法以期提高蚁群算法的效率及其准确率,优化全局搜索能力,并满足多种约束条件下的需求。实验结果表明:细菌觅食算法能够有效地解决一些蚁群算法上的缺陷,提高了蚁群算法的路径规划及其寻优能力且优于其他算法对蚁群算法的改进,证实了改进后的蚁群算法适应多种条件,路径求解的精度明显优于之前未改进的算法。

基于BFOA-PSO-GMM的轨道电路故障诊断研究

针对轨道电路系统庞大、故障种类繁多等问题,提出一种融合细菌觅食优化算法和粒子群优化算法的高斯混合模型,对轨道电路的多种故障类型进行诊断。该模型通过融合细菌觅食优化算法与粒子群优化算法,找寻适合EM算法的初始值,利用合适的初始值有效避免EM算法陷入局部最优,提高模型的故障诊断能力。通过对实测数据的训练和测试实验表明,本模型比传统高斯混合模型的故障诊断准确率提高了31.85%,比采用粒子群优化算法改进模型的故障诊断准确率提高了9.4%,即本模型对轨道电路的故障诊断更加有效。

“前港后厂”供应链“链-链”联盟利益分配

“前港后厂”模式下,港口为钢厂生产提供高质量的增值服务,形成了以港口服务供应链与钢厂制造供应链相辅相成的“链”与“链”联盟。而共生共赢的利益分配方案是影响联盟稳定与联盟绩效的关键因素。因此,为促进“前港后厂”供应链联盟有效运行,针对港口服务与钢铁制造“链-链”联盟的特点,考虑服务贡献度与服务时长差异,以联盟整体利益最大化为目标,构建了其利益分配模型。由于模型非线性、不可微,对结果精确性要求高,故选择搜索幅度广、进化空间大的细菌觅食优化算法求解。本文设计的觅食动力学与觅食优化算法相结合的新算法,克服了单一觅食优化算法翻转方向不确定导致其稳定性差的缺陷,提高了算法精确度及稳定性。使用Python语言仿真验证表明利益分配结果符合合作博弈理论中整体理性与个体理性原则,证明该利益分配模型公平高效,为制造业与服务业的“两业融合”提供了科学依据与理论方法。

基于改进蚁群算法的算力灵活迁移优化算法

针对现有云计算环境下国网数据中心资源调度存在的调度效率低、能源消耗高等问题,文章提出了一种基于改进蚁群算法的算力灵活迁移优化算法。首先构建国网云数据中心的算力迁移模型,对数据中心的资源调度能耗进行建模。然后通过引入细菌觅食算法改进基本蚁群算法的信息素初始化,并重新设计了启发函数和信息素挥发因子。仿真实验结果表明,与现有模型相比,文章的算法能够求出更优的算力资源调度方案,在减小任务完成时间的同时降低了国网数据中心36.6%的能耗。

基于暂态波形相似度的特高压输电线路故障定位技术

为提升输电线路故障定位精度,提出基于暂态波形相似度的特高压输电线路故障定位技术。根据输电线路正反向差电流与故障点电流的关联关系,采用Pearson关联系数分析故障数据正反向差电流波形细节处的相似度,构建故障测距函数,结合细菌觅食优化算法及改进蚁群算法优化故障测距函数,求解行波流经故障位置两侧的时间差,通过计算输电线路端点到故障点的距离,实现特高压输电线路故障定位。实验结果表明:该技术可实现对输电线路各位置、各类型故障的精准定位,且受过渡电阻、噪声的影响较小。

基于交互式细菌觅食优化算法的飞机基地维修人员调度

随着行业细分的逐步深入,航空公司将检修任务外包给基地维修服务提供商,以降低运营成本。针对外包模式下飞机基地维修人员调度问题,本文首先基于维修任务的分布式特征,结合维修服务提供商对成本控制的需求与维修人员对工作量分配公平的需求,构建调度模型。其次,提出与模型约束条件相结合的编码转换机制,基于结构重组思想以及群体内部信息交流方式设计交互式细菌觅食优化算法,并对模型进行求解。最后,基于分布式任务分配特点设计对比实验。实验结果表明,相较于原始细菌觅食优化算法、菌群优化算法、改进细菌觅食优化算法、粒子群算法、综合学习粒子群算法以及遗传算法,交互式细菌觅食优化算法具有更强的寻优能力、更高的搜索精度,以及良好的稳健性与收敛性能,能有效解决手动排班引起的耗时长、费力多等问题,提升飞机维修基地的运营效率。