Bacteria Foraging Optimization Research Articles

Optimization for position and rating of distributed generating units using bacteria foraging algorithm to reduce power losses

Distributed generation (DG) units, which include solar panels, wind turbines, and fuel cells, are gaining in popularity as a result of the fact that they are dependable and produce no harmful emissions when they generate electricity. However, the integration of these new power sources into the current power grid may result in several technical difficulties, including voltage fluctuations, power imbalances, and increased power losses. The use of optimization methods may assist to solve these difficulties by determining the best position and size of DG units. This will help to reduce the amount of power that is lost and will enhance the power system's overall performance. Therefore, the Bacteria Foraging Optimization (BFO) method was used for DG in this study. This approach helps to decrease phase jump during symmetrical and unsymmetrical faults, as well as power losses, controls voltage magnitude and angle, and regulates voltage magnitude. In addition, nonlinear restrictions that are regarded to be forward factors include voltage limitation, DG capacity limitation, and phase jump limitation. On the IEEE 69 bus radial distribution scheme, both single and double DGs are taken into consideration for the allocation process. Because of its worldwide convergence and decreased calculation liability, the BFO algorithm is used in the optimization process for both position and rating. The suggested procedure is tested on the IEEE 69 bus radial distribution system in a MATLAB environment; the results demonstrate the efficiency of the suggested approach. In this case, the proposed system was successful in achieving the triple line-to-ground fault-based phase jump of 26.3° without DG and 0.52° with DG; the line-to-line (AB) fault-based phase jump of 0.01° without DG and 0.001° with DG; the single line (A) to ground fault-based phase jump of 0.0027° without DG and 0.001° with DG; and the line to ground fault-based phase jump of 0.001° with DG.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.

Design and Optimization of an Ultrathin and Broadband Polarization-Insensitive Fractal FSS Using the Improved Bacteria Foraging Optimization Algorithm and Curve Fitting

A frequency-selective surface (FSS) optimization method combining a curve-fitting technique and an improved bacterial foraging optimization (IBFO) algorithm is proposed. In the method, novel Koch curve-like FSS and Minkowski fractal islands FSS were designed with a desired resonance center frequency and bandwidth. The bacteria foraging optimization (BFO) algorithm is improved to enhance the performance of the FSS. A curve-fitting technique is provided to allow an intuitive and numerical analysis of the correspondence between the FSS structural parameters and the frequency response. The curve-fitting results are used to evaluate the fitness function of the IBFO algorithm, replacing multiple repeated calls to the electromagnetic simulation software with the curve-fitting equation and thus speeding up the design process. IBFO is compared with the classical BFO algorithm, the hybrid BFO-particle swarm optimization algorithm (BSO), and the artificial bee colony algorithm (ABC) to demonstrate its superior performance. The designed fractal FSS is fabricated and tested to verify the experimental results. The simulation and measurement results show that the proposed FSS has a fractional bandwidth of 91.7% in the frequency range of 3.41-9.19 GHz (S, C, and X-bands). In addition, the structure is very thin, with only 0.025λ and 0.067λ at the lowest and highest frequencies, respectively. The proposed fractal FSS has shown stable performance for both TE and TM polarizations at oblique incidence angles up to 45°. according to simulations and measurements.

Optimal design of $ PI^\rho D^\mu $-controller for artificial ventilation systems for COVID-19 patients

<abstract><p>In light of the COVID-19 pandemic, many patients have suffered from Acute Respiratory Distress Syndrome (ARDS) in Intensive Care Units (ICUs) around the world. In the medical field, it is known that the so-called artificial ventilation device, which has become the mainstay of treatment of such syndrome, decreases mortality in critically ill COVID-19 patients. Due to the high reliability of this device, there is an emergency need to follow up the progress made on designing a robust controller for improving its performance. From this perspective, this work introduces different control design schemes for obtaining an optimal Fractional-order PID controller (or simply $ PI^\rho D^\mu $-controller) of the Artificial Ventilation (AV) system through two optimization algorithms: the Bacteria Foraging Optimization (BFO) and the Particle Swarm Optimization (PSO) algorithms. The realization of the controller is accomplished using four approximations: Oustaloup's approximation, the Continued Fractional Expansion (CFE) approximation and the $ 1^{st} $- and $ 2^{nd} $-order El-Khazali approximations. The validation of the controller design and the AV system behavior are verified via numerical simulation in order to demonstrate the effectiveness and the potency of all proposed schemes.</p></abstract>

An Adaptive Controller Design using Duelist Optimization Algorithm for an Interconnected Power System

A Controller is generally considered as a continuous and discrete mode of execution by a huge sample period that may result in degenerated dynamic performance or system instability. Nowadays, the maximum penetration in thermal, wind, hydropower systems has decreased the power system inertia that leads to rapid frequency response and higher frequency deviation followed by contingencies and requires rapid load frequency control. The goal of load frequency control (LFC) is to achieve zero steady-state errors in frequency deviations and minimize unscheduled tie-line power flows among the interconnected areas. The study of the literature reveals that a lot of research has been carried out in this area to achieve the desired objectives using different approaches. This manuscript proposes an optimization algorithm called Duelist Optimization Algorithm (DOA) in a three area interconnected power system consisting of thermal, wind, and hydro generating systems. The proposed system introduces an adaptive PID fuzzy controller whose parameters are optimized by the DOA algorithm. The Duelist Optimization algorithm is used to optimally tune the parameters of the controller in order to keep the system frequency deviation within the threshold limit, and maintain the power balance among the control areas during load variations. The proposed method is simulated in MATLAB / Simulink environment for the estimation of its performance and then compared with some of the existing techniques such as Artificial Bee Colony (ABC) optimization algorithm, Bacteria Foraging Optimization (BFO), and Particle Swarm Optimization (PSO) algorithm. The simulation result established the suprerioty of the proposed method over the other methods.

Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm and Fuzzy Entropy

To perform robust fruit image segmentation is a challenging task due to several variances in the varieties. Thresholding is one of the popular segmentation methods in recent times. The fuzzy entropy scheme has been mostly applied to image thresholding. Fuzzy membership functions are the major source for segmenting the image by fuzzy entropy and thresholding. In the proposed paper, the Teacher-Learner-Based Optimization (TLBO) algorithm is involved in searching for optimal threshold values combination for fruit image segmentation based on fuzzy entropy. The proposed scheme is performed on red apple, green apple, golden apple, guava, and orange fruit images. For comparison of segmentation results of the proposed scheme with existing approaches, PSNR and uniformity parameters are utilized. It has been observed that the performance of the proposed approach is more effective than GA (Genetic Algorithm), BFO (Bacteria Foraging Optimization), and HBMO (Honey Bee Mating Optimization) approaches.

Accurate estimation of modern power system harmonics using a novel LSA hybridized recursive least square technique

Abstract This paper purposes a new type of hybrid technique depends on lightning search algorithm (LSA) and recursive least square (RLS) named as LSA-RLS to overcome the harmonic estimation issues in time varying modern power system signals buried with noises. LSA is based on a natural phenomenon of lightning. It consists of three types of projectiles: transition, space and lead projectiles. Transition projectiles create population, space projectiles do the exploration and the lead projectiles do the work of exploitation and find the optimal solution. The basic LSA algorithm is mixed with RLS algorithm in an adaptive way to estimate the states of the harmonic signals. Simulation and validation are made with real time data obtained from a converter fed D.C motor drive. The efficacy of the proposed algorithm is verified by comparing the simulation results of recently reported algorithms such as particle swarm optimization (PSO), differential evolution (DE), bacteria foraging optimization (BFO), gravity search algorithm hybridized recursive least square method (GSA-RLS). It is verified that proposed (LSA-RLS) technique is the best in terms of computational time, convergence, accuracy.

Geographic Routing Scheme For Resource And Communication Efficiency In the IoT Ecosystem using Swarm Intelligence-based BFO Algorithm

Wireless sensor networks (WSNs) are the sensor nodes that are generally interconnected and these nodes communicate wirelessly to collect the data from the environment around them. Sensor nodes are low-powered and distributed in an ADHOC manner and in a decentralized mode. WSNs setup comes with some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for Internet of things (IoT) applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. The real-time applications of WSNs have potential design limitations such as energy and other QoS constraints in the context of IoT. This work basically formulates two different re-transmission strategies for optimized cross-layer design and also integrates the framework with a swarm intelligence technique assisted Bacteria Foraging Optimization (BFO) for efficient Cluster Head (CH) selection. The proposed work designs analytical modeling to realize the formulated concept and also simulates the computational design in a numerical computing tool. Finally, an extensive simulation is carried out using the MATLAB simulation tool. The obtained results show the effectiveness of the proposed system in terms of delay is reduced by 12%, throughput is increased to 10%, remaining energy of the network is increased to 11%, and packet delivery ratio of the proposed work in increased to 7-9% in comparison with the existing system.

Optimized adaptive noise canceller for denoising cardiovascular signal using SOS algorithm

Cardiovascular or electrocardiogram signals (ECG) are contaminated by various artefacts while recording which in turn degrades the quality of the vital information present in the signal. Hence, noise removal from ECG signal is an important matter of concern. In this paper, swarm intelligence techniques for the optimization purpose of adaptive filters/noise canceller (ANC) are utilized in the biomedical signal processing field. The analysis of results for de-noising ECG signals through adaptive filtration is presented using the symbiotic organisms search (SOS), particle swarm optimization (PSO) and harmony search (HS) algorithms which are applied to estimate and adjust the parameters of the adaptive filter in such a way to keep a close track of spontaneous variations of non–stationary signals in a faithful manner. From the simulation results, it has been observed that the ANC filter designed with the SOS technique achieves significant improvement in fidelity parameters, such as signal to noise ratio (SNR), mean square error (MSE), maximum error (ME), mean difference (MD), peak reconstruction error (PRE), normalized root mean squared error (NRMSE) and normalized root mean error (NRME) when compared with other reported techniques in the literature as well as the benchmark algorithms, namely PSO and HS. The SOS based ANC technique results in nearly 7 dB improvement in output SNR as compared to the recently reported ANC filter based on bacteria foraging optimization (BFO) algorithm and also provides a fair reduction of more than 90% in NRMSE and NRME when compared to other state-of-art denoising techniques. Furthermore, the ANC filter using the SOS technique enhances the correlation between the pure and reconstructed ECG signals.

Comparative performance analysis of hybrid differential evolution and pattern search technique for frequency control of the electric power system

In the current situation, operation and control of power system is a greater challenge. The most significant situation in power system control is load frequency control. In the present work, a hybrid differential evolution and pattern search (hDE-PS) method has been suggested for frequency regulation of electrical power systems. Fractional-order proportional integral derivative (FOPID) controller is implemented for design and analysis purpose. The suggested control method has been applied for two electrical power systems model, i.e., 2-area diverse source power system with/without HVDC linkage and 2-area thermal system. The performances of the suggested controller have been evaluated with PID and optimal controller. The simulation results indicate that system performances are enhanced with the suggested approach for identical structure. Robustness of the suggested approach has been analyzed by variation in random load and the system parameters. The suggested method (hDE-PS tuned FOPID) is further investigated with a 2-area thermal system. The performance of the recommended approach is analyzed by equating the results with other newly available approaches, like Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO), hybrid BFOA and PSO (hBFOA-PSO), multi-objective Non-dominated Sorting Genetic Algorithm (NSGA)-II and Firefly Algorithm for the similar structure.

A modified single and multi-objective bacteria foraging optimisation for the solution of quadratic assignment problem

Non-polynomial hard (NP-hard) problems are challenging due to time-constraint. The bacteria foraging optimisation (BFO) algorithm is a metaheuristics algorithm that is used for NP-hard problems. BFO is inspired by the behaviour of the bacteria foraging such as E. coli. The aim of BFO is to eliminate weak foraging properties bacteria and maintain breakthrough foraging properties bacteria toward the optimum. However, reaching to optimal solutions are time-demanding. In this paper, we modified single objective and multi-objective BFO (MOBFO) by adding mutation and crossover from genetic algorithm operators to update the solutions in each generation, and local tabu search algorithm to reach the local optimum solution. Additionally, we used fast non-dominated sort algorithm in MOBFO to find the best non-dominated solutions. We evaluated the performance of the proposed algorithms with quadratic assignment problem instances. The experimental results show that our approaches outperform some previous optimisation algorithms in both convergent and divergent aspects.

A modified single and multi-objective bacteria foraging optimisation for the solution of quadratic assignment problem

Non-polynomial hard (NP-hard) problems are challenging due to time-constraint. The bacteria foraging optimisation (BFO) algorithm is a metaheuristics algorithm that is used for NP-hard problems. BF...

PTS-PAPR Reduction Technique for 5G Advanced Waveforms Using BFO Algorithm

Non-orthogonal multiple access (NOMA) will play an imperative part in an advanced 5G radio arrangement, owing to its numerous benefits such as improved spectrum adeptness, fast data rate, truncated spectrum leakage, and, so on. So far, NOMA undergoes from peak to average power ratio (PAPR) problem, which shrinks the throughput of the scheme. In this article, we propose a hybrid method, centered on the combination of advanced Partial transmission sequence (PTS), Selective mapping (SLM), and bacteria foraging optimization (BFO), known as PTS-BFO and SLM-PTS. PTS and SLM are utilized at the sender side and divide the NOMA into several sub-blocks. An optimal phase factor is achieved by the BFA and combined with the NOMA sub-block, where a low peak power value is obtained. Further, we estimate the bit error rate (BER) and PAPR of BFA in the SLM and PTS technique. The simulation outputs reveal that the PTS-BFO outperforms the traditional peak power minimization approaches and moderates the complexity of the system. It is concluded that the proposed algorithm is not explored for the NOMA waveform.

Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and classification of plant diseases are more precise.

Heuristic Algorithm Assisted Controller Design for Nonlinear Type Liquid Level Process

In process control industries, controlling the liquid inside the non-linear tanks such as a conical tank, hopper tank and spherical tank is a highly challenging task due to the change in the cross section area of the tank. The aim of this project is to design a suitable controller to control the level of the hopper type tank by using conventional controllers like Ziegler-Nichols tuning method (ZN) and computational controllers using metaheuristic algorithms such as Bacteria Foraging Optimization (BFO) algorithm, Particle Swarm Optimization (PSO) algorithm and Firefly Algorithm (FA). This controller can be used for the industries that deal with this nonlinear type of process. The mathematical model for the conical tank was done to identify the transfer function of the system. The conventional and computational controllers were designed and implemented using MATLAB/Simulink to choose the best controller for the conical/hopper type tank liquid level process based on the time domain specifications such as rise time, settling time and peak overshoot percentage. Moreover, the performance of the system was analyzed based on the error indices such as Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Square Error (ITSE) and Integral Time Absolute Error (ITAE). Also, servo and regulatory operations were tested for the chosen best controller.

Resonance Image Enhancement using Hybrid Center Weighted Median Filter and Bacteria Foraging Optimization

Magnetic Resonance Image (MRI) is the best imaging technique employed nowadays for diagnosing brain tumour in the initial stage. This paper recommends an unique method for the brain MR image enhancement, that is centred on the Hybrid Center Weighted Median (HCWM) filter and Bacteria Foraging Optimization (BFO). The MR image for this research is obtained from the online and it is pre-processed to remove all the film artifacts. After that the high frequency components are eliminated from the MR brain image by means of a newly proposed HCWM filter. HCWM Filter is the hybrid filter derived by combining the Center Weighted Median Filter and the Weiner Filter. The swarm-based intelligence algorithm called the bacteria foraging optimization is used to predict the weights of the filter dynamically. The performance of the proposed filtering approach is evaluated with the other available filtering methods.

Performance optimisation of discrete time linear active disturbance rejection control approach

In the framework of the linear active disturbance rejection control (LADRC) approach, all the uncertainties, including the perturbed internal model parameters and time-varying external disturbances, can be estimated by constructing an extended state observer, and then cancelled in real time. However, the parameter tuning of the approach is an extremely challenging mission. In this paper, the model parameters of the controlled servo motor control system are identified by employing the algebraic parameter identification approach. Afterward, the bacteria foraging optimisation (BFO) algorithm, and the particle swarm optimisation (PSO) algorithm are both proposed to optimise the performance of the system driven by the LADRC approach in light of the identified model of the servo motor. The BFO and PSO algorithms and LADRC approach have been extensively applied in optimised control and networked systems. Extensive simulation results and experimental tests are given to demonstrate that the proposed approaches are effective and efficient for the performance optimisation of the LADRC approach.

Self-adaptive bacterial foraging algorithm based on estimation of distribution

Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy.

Parameterization of BFO Algorithm for the Improved Functionality of MFKM Technique for Better Pathological Identification in Brain MR Image

Intensity inhomogeneity, high level of noise, partial volume effect and poor image contrast are the major artefacts in medical image segmentation. Any of these artefacts might lead to unclear boundaries of tissues, hence the segmentation of tissues in the MR brain image cannot be determined with high accuracy, and this would be a problem to the radiologists to diagnose or to start the treatment because of the lack of facility to operate over the brain in in-vivo condition. This makes the radiologist and surgeons/experts to take time to come for the conclusion on pathology of a particular patient. So, the radiologists and experts need to give more exertion when this condition is applied for many patients at a day, to diagnose and to start treatment. To make this effortless to them, also for accurate diagnosis, this research paper provides an robust algorithm using the Modified Fuzzy K-Means (MFKM) and Bacteria Foraging Optimization (BFO) algorithm, which segments the abnormal tissues among the normal tissues from MR brain images with high accuracy. The accuracy of the Improved MFKM (IMFKM) algorithm is obtained in terms of Sensitivity and Specificity, and the proposed algorithm proves better segmentation results than the other conventional algorithms.

A hybrid approach for the optimization of quality of service metrics of WSN

The core objective behind this research paper is to implement a hybrid optimization technique along with proactive routing algorithm to enhance the network lifetime of wireless sensor networks (WSN). The combination of two soft computing techniques viz. genetic algorithm (GA) and bacteria foraging optimization (BFO) techniques are applied individually on destination sequence distance vector (DSDV) routing protocol and after that the hybridization of GA and BFO is applied on the same routing protocol. The various simulation parameters used in the research are: throughput, end to end delay, congestion, packet delivery ratio, bit error rate and routing overhead. The bits are processed at a data rate of 512 bytes/s. The packet size for data transmission is 100 bytes. The data transmission time taken by the packets is 200 s i.e. the simulation time for each simulation scenario. Network is composed of 60 nodes. Simulation results clearly demonstrates that the hybrid approach along with DSDV outperforms over ordinary DSDV routing protocol and it is best suitable under smaller size of WSN.