Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA–PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA–PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA–PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to ?50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA–PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.     

一种基于粒子群参数优化的改进蚁群算法

蚁群算法是一种应用广泛、性能优良的智能优化算法,其求解效果与参数选取息息相关.鉴于此,针对现有基于粒子群参数优化的改进蚁群算法耗时较大的问题,提出一种新的解决方案.该方案给出一种全局异步与精英策略相结合的信息素更新方式,且通过大量统计实验可以在较大程度上减少蚁群算法被粒子群算法调用一次所需的迭代代数.仿真实验表明,所提出算法在求解较大规模旅行商问题时具有明显的速度优势.     

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.     

蚁群算法的收敛速度分析

蚁群算法(ACO)作为一类新型的机器学习技术,已经广泛用于组合优化问题的求解,同时也应用于工业工程的优化设计.相对于遗传算法(GA),蚁群算法的理论研究在国内外均起步较晚,特别是收敛速度的分析理论是该领域急待解决的第一大公开问题.文中的研究内容主要是针对这一公开问题而开展的.根据蚁群算法的特性,该研究基于吸收态Markov过程的数学模型,提出了蚁群算法的收敛速度分析理论.作者给出了估算蚁群算法期望收敛时间的几个理论方法,以分析蚁群算法的收敛速度,并结合著名的ACS算法作了具体的案例研究.基于该文提出的收敛速度分析理论,作者还提出ACO-难和ACO-易两类问题的界定方法;最后,利用ACS算法求解TSP问题的实验数据,验证了文中提出的分析结论,得出了初步的算法设计指导原则.     

多目标资源优化分配问题的Memetic 算法

针对传统算法求解多目标资源优化分配问题收敛慢、Pareto解不能有效分布在Pareto 前沿面的问题, 提出一种新的Memetic 算法. 在遗传算法的交叉算子中引入模拟退火算法, 加强了遗传算法的局部搜索能力, 加快了收敛速度. 为了使Pareto 最优解均匀分布在Pareto 前沿面, 在染色体编码中引入禁忌表, 增加了种群的多样性, 避免了传统遗传算法后期Pareto 解集过于集中的缺点. 通过与已有的遗传算法、蚁群算法、粒子群算法进行比较, 仿真实验表明了所提出算法的有效性, 并分析了禁忌表长度和模拟退火参数对算法收敛性的影响.

     

Intelligent control of DC motor driven electromechanical fin actuator

In this paper modeling, simulation and control of an electromechanical actuator (EMA) system for aerofin control (AFC) with permanent magnet brush DC motor driven by a constant current driver are investigated. Nonlinear model of the EMA-AFC system has been developed and experimentally verified in actuator test bench. Model has been used as the starting point for PID position controller synthesis. To improve performances of the system, computational intelligence has been applied. Genetic PID optimization, genetic algorithm (GA) optimized fuzzy supervisory PID control and finally GA optimized nonlinear PID algorithm modification are proposed. Improved transient response and system behavior have also been experimentally validated.     

GA–PSO based vector control of indirect three phase induction motor

This paper deals with the genetic algorithm–particle swarm optimization (GA–PSO) based indirect vector control for loss minimization operation and optimal torque control of induction motor. It is estimated that more than around 50% of the world electric energy generated is consumed by electric machines such as induction motor, dc motor. So, optimal control strategy for minimum-energy loss in electric drives is important as one of improving efficiency. Relative to this aspect, the vector control of induction motor has been widely used to operate in a wide speed range by using flux weakening at rated speed. However, it is still necessary to advance in controller tuning because of coupling behavior between fluxes in motor. In this paper, tuning of speed controller and current controller in indirect vector control approach is performed using GA–PSO method on simulation and experiments. They reveal satisfactory results.     

CONTROL OF DEAD-TIME SYSTEMS USING HYBRID ANT COLONY OPTIMIZATION

This paper introduces two improved forms of the ant colony optimization (ACO) algorithm applied to a proportional integral derivative (PID) controller and Smith predictor design. Derivative free optimization methods, namely simplex derivative based pattern search (SDPS) and implicit filtering (IMF), are used to intensify the search mechanism in the ACO algorithm with improved convergence over the original ACO. The effectiveness of the controller schemes using the proposed algorithms, namely SDPS-ACO, and IMF-ACO, is demonstrated using unit step set point response for a class of dead-time systems, and the results are compared with some existing methods of controller tuning.     

The intelligent integrated speed controller of DTC for induction motor

The parameters selection of proportional coefficient and integral coefficient (PI) for speed controller is important for direct torque control system. However, it is difficult to adjust these parameters. In this paper, firstly, we use particle swarm optimization to search the appropriate PI values of the speed controller. Secondly, based on the optimized PI parameters, the fuzzy-PI speed control strategy is presented to solve the poor self-adaptability problem. Thus, the proportional coefficient k _p and integral coefficient k _i can be adjusted dynamically to adapt to the speed variations. And finally, to obtain the high-speed parallel processing ability, the well-trained RBF neural network replaces the fuzzy-PI speed controller. The comparison with conventional PI speed controller shows that the proposed intelligent integrated speed controller brings good benefits of fast speed response and good stability and reduces the torque ripple. The validity of the proposed intelligent integrated speed controller is verified by the simulation results.     

基于遗传算法与蚁群算法动态融合的网格任务调度

深入分析遗传算法和蚁群算法的机理,并结合网格任务调度的研究,提出基于遗传算法和蚁群算法动态融合的网格任务调度策略.该策略通过不同迭代次数中种群相似度的差值实现两种算法的动态融合.仿真实验表明该策略是可行的,并且具有高效性.     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems

In this paper, a novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed for Load Frequency Control (LFC) of multi-area power system. Initially a two-area non-reheat thermal system is considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS (hDEPS) optimization technique. The superiority of the proposed controller is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as DE, Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI controllers for the same interconnected power system. Furthermore, robustness analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to multi-area multi-source power system with/without HVDC link and the gains of fuzzy PID controllers are optimized using hDEPS algorithm. The superiority of the proposed approach is shown by comparing the results with recently published DE optimized PID controller and conventional optimal output feedback controller for the same power systems. Finally, Reheat turbine, Generation Rate Constraint (GRC) and time delay are included in the system model to demonstrate the ability of the proposed approach to handle nonlinearity and physical constraints in the system model.     

PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness.

     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

基于蚁群算法的短波侦收资源调度方法研究

为解决短波侦收中任务执行时间长和侦收资源利用率低等问题,以最大侦收概率为目标,结合约束条件建立短波协同侦收资源调度模型,设计运用改进型蚁群优化算法对模型求解,采用粒子群参数优化技术改进蚁群优化算法,利用全局异步与精英策略相结合的信息素更新策略,使算法具有更强的寻优能力和运算速度,不仅提高了系统资源利用率而且能够快速确定出最佳调度方案。实验结果验证了所提方法的可行性和有效性。     

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is mea sured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control (AGC) plays a crucial role. In this paper, multi-area (Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative (PID) controller as a supplemen tary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm (FFA). The experimental results demonstrated the comparison of the proposed system performance (FFA-PID) with optimized PID controller based genetic algorithm (GA PID) and particle swarm optimization (PSO) technique (PSO PID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error (ITAE) cost function with one percent step load perturbation (1% SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.      

Fuzzy entropy based optimal thresholding using bat algorithm

Image segmentation is a very significant process in image analysis. Much effort based on thresholding has been made on this field as it is simple and intuitive, commonly used thresholding approaches are to optimize a criterion such as between-class variance or entropy for seeking appropriate threshold values. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. This paper considers image thresholding as a constrained optimization problem and optimal thresholds for 1-level or multi-level thresholding in an image are acquired by maximizing the fuzzy entropy via a newly proposed bat algorithm. The optimal thresholding is achieved through the convergence of bat algorithm. The proposed method has been tested on some natural and infrared images. The results are compared with the fuzzy entropy based methods that are optimized by artificial bee colony algorithm (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO); moreover, they are also compared with thresholding methods based on criteria of between-class variance and Kapur's entropy optimized by bat algorithm. It is demonstrated that the proposed method is robust, adaptive, encouraging on the score of CPU time and exhibits the better performance than other methods involved in the paper in terms of objective function values.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

求解多维背包问题的蚁群-拉格朗日松弛混合优化算法

针对多维背包问题(MKP) NP-hard、约束强的特点, 提出一种高效的蚁群-拉格朗日松弛(LR) 混合优化算法. 该算法以蚁群优化(ACO) 为基本框架, 并基于LR 对偶信息定义了一种MKP效用指标. ACO使得整体算法具有全局搜索能力, 所设计的效用指标将MKP的优化目标与约束条件有机地融合在一起. 该指标一方面可以用来定 义MKP核问题, 降低问题规模; 另一方面, 可以用作ACO的启发因子, 引导算法在有希望的解区域中强化搜索. 在大量标准算例上的测试结果表明, 所提出算法的鲁棒性较好; 与其他已有算法相比, 在求解质量和求解效率方面均具有很强的竞争力.

     

基于改进蚁群算法的模糊控制器优化设计

研究控制器优化问题,由于模糊控制系统参数无法同时优化,使得系统选择参数困难,使系统控制效果存在一定的缺陷,安全性和可靠性降低。为解决上述问题,提出了一种多种群进化蚁群算法对模糊控制器优化设计。采用懒蚂蚁效应的改进蚁群算法进行优化,在传统蚁群算法的基础上,采用多个种群并行,对算法的初始化、路径构建以及信息素更新改进,并引入到模糊控制器的隶属函数、模糊规则的优化搜索中,搜索出适应于不同控制阶段的模糊控制器参数及控制规则,并进行仿真。仿真结果证明了改进算法对模糊控制器的参数具有良好的搜索速度和精度,使系统有很强的鲁棒性。