Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

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.

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.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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.

A new dynamic bacterial foraging optimization and its application on model reduction

Inspired by the foraging behavior of E.coli bacteria,bacterial foraging optimization(BFO)has emerged as a powerful technique for solving optimization problems.However,BFO shows poor performance on complex and high-dimensional optimization problems.In order to improve the performance of BFO,a new dynamic bacterial foraging optimization based on clonal selection(DBFO-CS)is proposed.Instead of fixed step size in the chemotaxis operator,a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process,which can improve convergence speed.Furthermore,reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected,which can enhance convergence precision.Then,a set of benchmark functions have been used to test the proposed algorithm.The results show that DBFO-CS offers significant improvements than BFO on convergence,accuracy and robustness.A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented.Results show that the proposed algorithm can efficiently approximate the systems.

Comparison of particle swarm and bacterial foraging optimization algorithms for therapy planning in HIV/AIDS patients

In HIV/AIDS patients, antiretroviral therapy （ART） is used for reducing the viral load and helps in increasing the life span of the individual. However, severe side effects are associated with the use of antiretroviral drugs. Hence, a treatment schedule, using minimal amount of drugs, is required for maintaining a low viral load and a healthy immune system. The objective of this work is to compute the optimal dosage of antiretroviral drugs for therapy planning in HIV/AIDS patients, using intelligent optimization techniques. In this work, two computational swarm intelligence techniques known as the particle swarm optimization （PSO） and bacterial foraging optimization （BFO） in conjunction with the three-dimensional mathematical model of HIV/AIDS have been used for estimating the optimal drug dosage for administering therapy by minimization of viral load as well as the total drug concentration. Results demonstrate that, using the proposed method, it is possible to achieve minimal viral load and an improved immune system, with the estimated drug dosage. Further, it was observed that the efficiency of BFO （CD4 cells = 757 cells/mm^3 at seventh year of infection） for estimation of optimal drug dosage is higher than the PSO method （CD4 cells = 817 cells/mm^3 at seventh year of infection）. This work seems to be of high clini- cal relevance since, at present, ART is the widely used procedure for treatment of HIV infected patients.

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。

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。

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。

IRS辅助大规模MIMO系统中抑制残余硬件损伤的AQBFO无源波束赋形方案

由通信收发机硬件非理想特性导致的残余硬件损伤在智能反射面(Intelligent reflecting surface,IRS)辅助的大规模多输入多输出(Multiple-input multiple-output,MIMO)系统中难以避免,并且会严重降低上行用户的可达和速率。针对这一问题,本文提出了一种基于自适应量子菌群觅食优化(Adaptive quantum bacterial foraging optimization,AQBFO)算法的无源波束赋形方案,用于抑制残余硬件损伤对系统性能的影响。首先,基于统计信道状态信息(Channel state information,CSI)推导出系统上行可达和速率的近似解析表达式。然后,以最大化和速率为目标,基于AQBFO算法对无源波束赋形进行优化。仿真结果验证了在IRS辅助大规模MIMO系统中,基于AQBFO算法的无源波束赋形方案能够有效抑制残余硬件损伤的影响,并显著提升系统的上行遍历和速率。

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。

去蜂窝大规模多输入多输出非正交多址系统中基于量子菌群优化的接入点选择方案

去蜂窝大规模多输入多输出非正交多址(MIMO-NOMA)系统的接入点(AP)选择,对有效降低系统的回程链路开销、提高用户的下行可达速率影响较大。该文针对AP选择的去蜂窝大规模MIMO-NOMA系统下行链路建立了用户平均速率的表达式;在此基础上,提出了基于量子菌群优化(QBFO)的AP选择方案,将AP与用户的连接关系以量子比特的形式编码,利用自适应量子旋转门模拟细菌趋化,实现细菌位置更新,通过对量子细菌种群进行测量,获得AP与用户的选择解集,并引入驱散操作避免算法陷入局部最优。实验结果表明,所提方案能够在降低回程链路开销的同时显著提高用户的下行平均速率,相较于基于接收功率和基于信道估计均方误差的AP选择方案,该文所提方案在降低用户间干扰、提高系统总吞吐量方面表现更优。

基于IPSO-BP神经网络的WSNs数据融合算法

针对无线传感器网络(WSNs)数据融合算法中反向传播(BP)神经网络存在对初值敏感、收敛速度慢、易陷入局部最优解等问题,提出基于改进粒子群优化BP(IPSO-BP)神经网络的WSNs数据融合算法。首先,用细菌觅食算法的趋化、迁徙算子对粒子群优化(PSO)算法进行改进;然后,用IPSO算法优化BP神经网络的权值和阈值,再引入到WSNs数据融合中,簇成员节点负责采集监测数据,在簇首节点通过优化后的BP神经网络对数据进行特征提取,并将融合结果发送至汇聚节点。仿真结果表明:IPSO-BP算法能有效提高融合精度和收敛速度,减少冗余数据传输,延长网络生命周期。

基于细菌觅食算法的主动磁悬浮轴承PID控制优化

针对磁悬浮轴承系统采用传统PID控制时调节速度慢、超调量大的问题,建立了磁悬浮轴承系统模型并通过细菌觅食优化算法不断迭代优化控制参数K_(p),K_(i),K_(d)。仿真分析结果表明,与传统及粒子群优化PID控制相比,细菌觅食优化PID的超调性能更好,响应速度更快,能满足磁悬浮电动机低振动、高精度的性能要求。

基于SMABC算法的FPRM逻辑电路面积优化

固定极性Reed-Muller(FPRM)逻辑电路面积优化是当前集成电路设计领域的研究热点。但现有FPRM逻辑电路面积优化方法存在优化效率低和优化效果差等问题。FPRM逻辑电路面积优化属于组合优化问题,提出一种自适应混合人工蜂群(SMABC)算法。所提算法在引领蜂搜索阶段引入细菌觅食算法中的细菌趋化行为,使引领蜂向靠近优秀蜜源的方向搜索,提高了所提算法的收敛速度;对跟随蜂的选择概率进行改进使其依据种群的变化自适应改变,提高了所提算法的全局搜索能力;对侦查蜂的转换条件进行改进,增加了侦查蜂在进化过程中的扰动幅度;且在进化过程中引入精英保留策略以提高种群质量。此外,提出一种基于SMABC算法的FPRM逻辑电路面积优化方法,所提方法收敛速度最快且面积优化率最高为54.62%,平均面积优化率为15.33%。