Hybrid Particle Swarm Optimization Bacterial Foraging control an agricultural Quadcopter

In order to solve the parameter optimization problem of traditional power system stabilizer, a novel power system stabilizer (PSS) design method is proposed based on synergy of bacterial forging and particle swarm optimization algorithm. Bacterial foraging algorithm may lead to delay in reaching global solution. Particle swarm optimization may lead to entrapment in local minimum solution and obtain imprecise search results. The new algorithm is proposed to combines both algorithms' advantages in order to get better optimization values. A coordinate optimization index based on multi-object and multiple operation conditions is presented so as to improve the damping ratios of electromechanical modes and increase the robustness of power system. In this paper, PSS design for single machine infinite bus is formulated as multi-objective and multi-operating conditions, and the hybrid approach involving bacterial foraging and particle swarm optimization algorithm is employed to solve this problem. The results of both eigenvalue analysis and nonlinear simulation show that the proposed PSS can damp the low-frequency oscillations effectively and work well with high control performance under different operating conditions. Compared with PSS which is design by genetic algorithm, the proposed PSS in this paper has better damping characteristics.

This paper presents an application of a hybrid technique combining bacterial foraging with particle swarm optimization (BF-PSO) for determining the optimal values for the proportional-integral-derivation (PID) controller for a load frequency control (LFC) of two-area interconnected power system. This method is compared to the heuristic optimization method (PSO) and to bacterial foraging optimization (BFO) thereby to the traditional Ziegler-Nichols method. The results of PID-tuning are compared with the results of I, ID and PI controllers. The transient responses are shown due to step load disturbances in area-1. The main primary objective is to suppress all the fluctuations of the system frequency and tie line power flow.

Many practical applications such as medical image segmentation, object detection, recognition tasks and video surveillance have need for accurate image segmentation techniques. Hence image segmentation is an important technique for image processing which is regarded as first step for image analysis. In this paper an image segmentation technique based on Bacterial Foraging (BF) and Particle Swarm Optimization (PSO) algorithm is addressed. Initially adaptation is done on BF algorithm by computing the step length using the number of variables in the search space. Further, on exhaustive analysis of BF algorithm, it was revealed that the tumble behavior will lead to random delay in searching optimal solutions and premature convergence. This synergy algorithm makes use of PSO in providing social information and adaptive BF algorithm in finding new optimal threshold values using elimination and dispersal. The proposed method has been applied to few benchmark images with promising results.

Bacterial Foraging (BF) and Particle Swarm optimizer (PSO) are two fertile and robust optimization methods. The synergy of these two optimizers improves their search capability while overcoming their handicaps as premature convergence and parameter dependency. A Multi-Objective hybrid Bacterial Foraging Particle Swarm (MOBFPS) optimizer is applied to maintain the stability and the security of the electrical power supply post disturbances/faults via minimizing the amount of the dropped load while increasing the lowest swing frequency. In this research, the Under-Frequency Load Shedding (UFLS) scheme based on MOBFPS was manipulated as bounded optimization problem with limits depicting the boundaries of the power system state variables. The viability of MOBFPS is validated against traditional approach, BF and PSO UFLS methods. Two different test cases, IEEE 9-bus and 39-bus systems, are considered for investigating the performance of the proposed UFLS schemes. The test systems are subjected to variety of the operating scenarios as: lose of single/multiple plants and substantial abrupt load increase. The IEEE 9- and 39-bus systems are simulated in the DigSilent power factor software, whereas MATLAB code was used for traditional, BF, PSO and MOBFPS. MOBFPS provided the highest swing frequency and the lowest dropped load amount. Moreover, the computation requirements of MOBFPS are comparable with PSO.

Bacterial Foraging Optimization algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of optimization process. In the classical BFOA proposed by Passino, during the process of chemotaxis, optimization depends on a random search direction which may lead to delay in reaching global solution. To accelerate the convergence speed of group of bacteria near global optima the chemotactic step has been made adaptive and the resultant is Adaptive Bacterial Foraging Optimization (ABFO). In order to overcome the delay in optimization and to further enhance the performance of ABFO, this paper proposed a new hybrid algorithm combining the features of Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO) for tuning a Fractional order Proportional Integral speed controller in a vector controlled Permanent Magnet Synchronous Motor Drive. Our tuning method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. Computer simulations illustrate the effectiveness of the proposed approach compared to that of classical methods and state of art optimization techniques like PSO and ABFO.

—The proportional-integral-derivative controllers were the most popular controllers of this century because of their remarkable effectiveness, and simplicity of implementation. However, proportional-integral-derivative controllers are usually poorly tuned in practice. This article presents a hybrid particle swarm optimization and bacterial foraging techniques for determining the optimal parameters of a proportional-integral-derivative controller for speed control of a permanent magnet brushless DC motor. The first part of the article deals with the system modeling and its verification where a model of modest accuracy cannot be expected to give a fair comparison of different controllers. The remaining parts of the article present the application of different optimization techniques to tune the proportional-integral-derivative controller as applied to the motor model. The particle swarm optimization, bacterial foraging, and bacterial foraging-particle swarm optimization algorithms are implemented in MATLAB while the GA Toolbox is used. The performance of the tuned controllers is simulated and experimentally verified to evaluate the main characteristics of each one. It is found that the proposed hybrid bacterial foraging-particle swarm optimization technique is more efficient in improving the step response characteristics and achieving the desired performance indices.

A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO 2 ) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO 2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO 2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO 2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO 2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO 2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO 2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.

Identification of flight state under different simulator modes using improved diffusion maps

Hybrid BF–PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging–Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg–Marquardt Neural Network classifier.

Swarm robots cooperating in a group offer plentiful benefits and can accomplish several jobs that could be otherwise challenging either for human beings or for a single robot. Here we have considered two evolutionary based control algorithms, namely Bacterial Foraging (BFOA) and Particle swarm optimization (PSO), for flocking of a swarm to a predefine objective along an optimum path while avoiding obstacles. During the movement of the swarm, attraction, repulsion and formation coefficients of all agents are evaluated using the above stated algorithms based on some fitness function as described. The evaluated coefficients can plan the path with obstacle avoidance efficiently throughout the journey. It is shown that PSO is responsible for proficient and fast path selection whereas BFOA maintains formation throughout the trajectory. Simulation results illustrate that two competing algorithms produce robust solution in different aspects.

A hybrid optimization algorithm for improving load frequency control in interconnected power systems

Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators

Fog computing minimizes latency and bandwidth consumption by processing data near the source, but it has the challenge of workload balancing across the dynamic and resource limited fog nodes. Uneven task assignment can result in bottlenecks, idle resources, and lowered Quality of Service (QoS) standards. In this work, we introduce a hybrid metaheuristic load balancing algorithm for fog computing by combining the Bacterial Colony Optimization (BCO) and the Particle Swarm Optimization (PSO) techniques to enhance load balancing. BCO has good solution space exploration capabilities, whereas PSO has the advantage of quick convergence; the hybrid takes advantage from both to achieve reduced makespan with better VM usage. The proposed algorithm has been developed in the Python language, with original BCO and hybrid modules, along with a standard PSO executable. The program has been tested on a synthetic offline task-VM dataset generated by CloudSim 6.0 with size ranges from 100 to 10,000 tasks and poison distribution of variety of tasks arriving, with identical experiment settings. The results indicate the hybrid BCO-PSO to exhibit significant makespan reduction with increased VM utilization compared to the individual BCO, PSO, as well as the Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) algorithms for most test scenarios, with faster convergence for high workload scenarios. In the high-load cases, the hybrid reduced makespan by 32.76% compared to AIW-PSO for 5000 tasks and by 35.79% compared to AIW-PSO for 10000 tasks. These experimental results indicate that the proposed hybrid algorithm can be an effective, adaptive solution for task allocation in fog-inspired computational scheduling scenarios. This evaluation focuses on computation-side task scheduling using a synthetic task-VM model and keeping network-level factors such as latency or bandwidth idle.