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.

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。

基于BFOA的拉格朗日插值点最优配置

拉格朗日插值法是工程中常用的函数近似重构技术,其有效性与采样点的位置选择密切相关。细菌觅食优化算法(BFOA)则是一种新兴的模拟大肠杆菌觅食行为的群体智能算法,目前尚未得到广泛的应用。提出利用细菌觅食优化算法进行拉格朗日插值点的优化求解,结果优于传统方法。本研究验证了细菌觅食优化算法的有效性,同时为拉格朗日插值点的最优配置提供了新的解决思路。

基于BFOA算法的配电网DG选址定容方法

发展分布式能源系统对于实现的“碳达峰”和“碳中和”,提升可再生能源的开发利用具有重要意义。提出一种基于细菌觅食优化算法(BFOA)的配电网分布式电源(DG)选址定容方法。建立以配电网的功率损耗指数、电压偏差以及安装分布式电源所降低的净运行成本最小为目标的数学模型及约束条件,提出损耗敏感系数(LSF)来确定DG安装位置,并引用BFOA算法求解DG的最佳容量。仿真表明,相对于传统优化算法,BFOA算法在模型求解时间和收敛速度上具有明显优势,所提规划方法能够最大限度地降低功率损耗和运行成本,并提高系统的电压稳定性。

基于BFOA-SVR的铁路车站环境温度预测

铁路车站是人群密集的大型公共建筑,站内温度易受外界温度与站内湿度、光照、人群等多种动态复杂因素的影响。针对站内温度的预测具有时变性、复杂性、非线性等特点,因此提出了一种基于细菌觅食优化算法(Bacterial-Foraging Optimization Algorithm,BFOA)的支持向量回归(Support Vector Regression,SVR)的铁路车站环境温度预测。首先将环境数据作为特征向量输入,并通过BFOA算法对SVR模型的进行参数优化,最后基于优化模型开展温度预测,并与GA、PSO算法优化模型进行仿真对比。实验结果表明,相较于其它模型,该模型具有更好的预测精准度和鲁棒性,预测结果可以为车站环境调控与设备节能提供决策依据。

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。

基于细菌觅食法求解单目标约束优化问题

本文应用细菌觅食算法去求带有约束的优化问题,该算法是使用罚函数法将单目标约束优化问题转化为无约束优化问题来进行求解,即利用原函数和约束函数构造一个新目标函数,再用细菌觅食算法对该新目标函数进行优化,该算法因具有群体智能算法并行搜索、易跳出局部极小值等优点,不断地寻找更优可行解,逐渐达到搜索全局最优解。数值仿真实验结果表明该方法求解带有约束优化问题是可行的,同时也验证了该算法的有效性。

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。

“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。

采用改进细菌觅食算法的风/光/储混合微电网电源优化配置

风能和太阳能具有随机性和波动性的特点,由分布式电源、储能装置、负荷组成的微电网协调运行与控制十分复杂,对孤岛运行的微电网合理地配置电源以提高供电可靠性、经济性是微电网规划建设的一个首要问题。将改进的细菌觅食算法(bacterial foraging algorithm,BFA)应用到解决风/光/储混合的全年孤岛运行的微电网电源优化配置问题中,建立了计及设备投资成本、运行和维护成本、燃料成本、环保折算成本的微电网电源优化配置模型,以年风速、气温、光照强度作为输入,根据不同的用户供电可靠性和备选电源要求,得到微电网电源的类型及其容量的最优方案,结果表明改进的细菌觅食算法具有全局最优搜索能力强、寻优速度快的特点。该方法可以全面评估各种分布式电源的经济性,根据微电网建设地点的气象条件、投资成本等降低微电网电源的冗余投资,满足用户定制的多样化可靠性要求。