Bacteria Foraging Optimization Algorithm Research Articles

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

Segmentation and classification of medical big data on brain tumor using bacteria foraging optimization algorithm along with learning automata

AbstractDetecting the early stage of brain tumors is significant for an effective therapy that can probably minimize the death rate of patients affected from brain tumors. Magnetic resonance imaging is the benchmark standard for diagnosing brain cancers but, it was difficult to split and categorize different kinds of brain tumors due to the delicate arrangement of the brain's anatomy. To overcome these difficulties and provide an effective classification, this research introduced a hybridized optimization technique which enhance the performance of the classifier. The hybridization is done between Bacteria Foraging Optimization Algorithm (BFOA) along with Learning Automata (LA), these two techniques improve the search speed and automate the learning capability of Convolutional Neural Network (CNN) classifier. The obtained results show that proposed BFOA LN‐CNN classifier better than the existing Deep Convolutional Neural Network (DCNN) based on Improved Harris Hawks Optimization (HHO) known as G‐HHO and Improved Invasive Bat (IIB)‐based Deep Residual network model. The classification accuracy of the proposed method is 99.41% whereas the DCNN‐G‐HHO and IIB deep residual neural network provides accuracy of 97% and 91.9% respectively.

Anti-intrusion strategy MANET inspired by the bacterial foraging optimization algorithm

Mobile ad hoc networks (MANET) are reflexivity, fast, versatile wireless networks that are particularly useful when traditional radio infrastructure is unavailable, such as during outdoor events, natural disasters, and military operations. Security may be the weakest link in a network due to its dynamic topology, which leaves it susceptible to eavesdropping, rerouting, and application modifications. More security problems with MANET exist than with its service Quality of Service (QoS). Therefore, intrusion detection, which controls the system to find further security issues, is strongly suggested. Keeping an eye out for intrusions is essential to forestall future attacks and beef up security. If a mobile node loses its power supply, it may be unable to continue forwarding packets, which depends on the structure's condition. The proposed study presents a security of trust and optimization that conserves energy methods for MANETs based on integrating the K means algorithm and Bacteria Foraging Optimization Algorithm (KBFOA). This work proposes a method for quickly and accurately determining which nodes should serve as Cluster Heads (CHs) by K means algorithm. This approach aims to choose a node with a high Sustainable Cell (SC) rate as the Header of a Cluster (HC). Each node will calculate its SC according to its unique factors, such as energy consumption, degree, remaining energy, mobility, and distance from the HC and base stations. The security of MANETs is improved by the inclusion of an algorithm in the proposed approach that detects and eliminates rogue nodes. This suggested approach will increase network stability and performance using a high-sustainable cluster head. The proposed approach will be achieved the highest reliability of clustering rate of 97%. The intended research will be accomplished Maximum security measures rate will be accomplished by 96%, the maximum rate of data transmission will be obtained of 96%, and greater efficient detection of malicious nodes ratio will be enhanced by 95%, lower energy consumption rate will be achieved by 0.09 m joules.

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.

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.

Load-Frequency Control of Three-Area Interconnected Power Systems with Renewable Energy Sources Using Novel PSO~PID-Like Fuzzy Logic Controllers

Modern era power systems may include not only traditional primary energy sources like hydro or thermal energy, but also a variety of Renewable Energy (RE) sources such as solar and/or wind power. This leads to the complexity of the electrical networks related to their design and construction as well as system stability and control issues. Considered to be one of the most crucial control issues, Load Frequency Control (LFC), must be continuously improved in order to ensure the control goals. For an interconnected power system, the control purposes are to maintain the net frequency at nominal value, e.g. 50 or 60Hz as well as to ensure that tie-line power flows are stable at scheduled values. This work proposes a novel LFC strategy applying Particle Swarm Optimization (PSO) ~ PID – like fuzzy logic–based controllers. PSO is one of the most effective optimization techniques. It is used to optimally determine four scaling factors for each LFC proposed in this study. A three-area power network consisting of a hydraulic station, a non-reheat plant, and a reheat unit along with RE sources such as wind and solar power are taken into consideration. The control performance of the proposed control strategy is compared to those of existing controllers, i.e. Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Fractional Order-PID (FPID), and fuzzy logic-based PI controllers for the same interconnected power grid model with various case studies of load changes along with nonlinearities and different RE source conditions. Simulation results implemented in MATLAB/Simulink demonstrate the feasibility and applicability of the proposed control strategy.

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.

Simulation and hardware-in-the-loop real time testing of different controllers for frequency regulation of electrical power systems

In this work, various controllers, such as hybrid PID with Filter & Fractional-Order PD (hPIDN-FOPD), hPIDN-PD and PID are recommended for frequency control problems. The analysis is prepared for 2 electrical power system (PS) models i.e. 2-area and 3-area thermal systems with physical restraints. The benefit of the suggested Differential Evolution (DE) based hPIDN-FOPD controller has been shown by relating its performance with some newly published approaches, like Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Firefly Algorithm (FA) and hybrid BFOA & Particle Swarm Optimization (hBFOA-PSO) techniques. The recommended approach is supplementarily investigated to a 3-area thermal PS with nonlinearity. The analysis reveals on the comparison that DE based hPIDN-FOPD offers improved results related to PID, hPIDN-PD and newly available FA tuned PID controller. The experimental authentication with the Hardware-in-the-Loop (HIL) real time (RT) simulation carried out on the OPAL-RT-OP5700 simulator.

A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANET) are self-organizing, rapidly deployable wireless networks excellent for outdoor events, communications in places lacking radio infrastructure, disasters, and military activities. Because network topologies are flexible and dynamic, security may be the most vulnerable point in the network, open to attacks including eavesdropping, routing, and application changes. MANET has more security flaws than quality of service (QoS). It is thus recommended to use intrusion detection, which regulates system to detect further security problems. Monitoring for intrusions is crucial for prevention and additional security against unwanted access. The loss of a mobile node’s power source may affect the node’s ability to forward packets, which is reliant on the system’s overall life. In this paper, the Bacteria for Aging Optimization Algorithm (BFOA), which finds the ideal hops in advancing the routing, is utilized to offer a trust-based protected and energy-efficient navigation in MANETs using a trust-based protected and energy-efficient navigation algorithm. The fuzzy clustering algorithm is activated first, and the Cluster Heads (CHs) are selected depending on the value of indirect, direct, and recent trust that each CH has. In addition, value nodes were discovered based on trust levels. Moreover, the CHs are engaged in multi hop routing, and the selection of the ideal route is based on the projected protocol, which selects the best routes based on latency, throughput, and connection within the course’s boundaries. Even without an attack, compared to the exiting methods EA-DRP & EE-OHRA the proposed secure optimization routing (BFOA) algorithm produced a minimum energy of 0.10 m joules, a minimal latency of 0.0035 m sec, a maximum throughput of 0.70 bps, and an 83 percent detection rate, with enhanced results obtained by using a selective packet dropping attack

Grasshopper Optimization Algorithm-Based Fuzzy-2DOF-PID Controller for LFC of Interconnected System With Nonlinearities

The load frequency control (LFC) is an automation scheme employed for an interconnected power system to overcome the frequency deviation issue because of load variation in the most economical way. This work puts an earliest effort to study the LFC issue of a three-area power systems including nonlinearities using fuzzy-two degree of freedom-PID (F-2DOF-PID) controller optimized with grasshopper optimization algorithm (GOA). Initially, GOA optimized PID controllers are considered for a two area non-reheat thermal system including generation rate constraint to validate the superiority over PID controllers tuned with some recently reported optimization techniques, such as hybrid firefly algorithm-pattern search, firefly algorithm, bacteria foraging optimization algorithm, genetic algorithm, and conventional Ziegler Nichols technique. Then the work is reconsidered for the same system to verify the supremacy of F-2DOF-PID controller over other controllers such as fuzzy-PID, two degree of freedom-PID, and PID with GOA framework. Furthermore, the study is extended to a three-area system considering the effect of nonlinearities to verify effectiveness and robustness of proposed controller.

Design and analysis of the 2DOF-PIDN-FOID controller for frequency regulation of the electric power systems

In this study, a new controller structure such as 2- Degree Of Freedom Proportional–Integral-Derivative through Filter and Fractional-Order Integral-Derivative (2DOF-PIDN-FOID) has been suggested for the regulation of frequency. To optimise the controller gains, a differential evolution (DE) technique has been employed. The investigation is made for two models, i.e. 2-area thermal and 2-area multi-source thermal-hydro-gas systems. The performance of the 2DOF-PIDN-FOID is analysed by equating the results with other newly available approaches, like Genetic Algorithm (GA), Bacteria Foraging Optimisation Algorithm (BFOA), hybrid BFOA and Particle Swarm Optimisation (hBFOA-PSO) and Firefly Algorithm (FA) for the similar structure. The suggested scheme is further examined with the second test system. Investigations reveal on the comparison that DE-tuned 2DOF-PIDN-FOID provides better results compared to recently published approaches. The experimental justification with real-time simulation has been carried out on the OPAL-RT simulator. It is noticed that real-time results closely match with simulation results.

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.

Bio-Inspired Workflow Scheduling on HPC Platforms

Efficient scheduling of tasks in workflows of cloud or grid applications is a key to achieving better utilization of resources as well as timely completion of the user jobs. Many scientific applications comprise several tasks that are dependent in nature and are specified by workflow graphs. The aim of the cloud meta-scheduler is to schedule the user application tasks (and the applications) so as to optimize the resource utilization and to execute the user applications in minimum amount of time. During the past decade, there have been several attempts to use bio-inspired scheduling algorithms to obtain an optimal or near optimal schedule in order to minimize the overall schedule length and to optimize the use of resources. However, as the number of tasks increases, the solution space comprising different tasks-resource mapping sequences increases exponentially. Hence, there is a need to devise mechanisms to improvise the search strategies of the bio-inspired scheduling algorithms for better scheduling solutions in lesser number of iterations/time. The objective of the research work in this paper is to use bio-inspired bacteria foraging optimization algorithm (BFOA) along with other heuristics algorithms for better search of the scheduling solution space for multiple workflows. The idea is to first find a schedule by the heuristic algorithms such as MaxMin, MinMin, and Myopic, and use these as initial solutions (along with other randomly generated solutions) in the search space to get better solutions using BFOA. The performance of our approach with the existing approaches is compared for quality of the scheduling solutions. The results demonstrate that our hybrid approach (MinMin/Myopic with BFOA) outperforms other approaches.

Fractional Order Fuzzy PID Controller for Automatic Generation Control of Power Systems

In this study, a Hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) tuned Fractional Order Fuzzy PID (FOFPID) structure is suggested for AGC of power systems. At first, a non-reheat type two-area thermal system is considered and the improvement of the proposed approach over Bacteria Foraging Optimization Algorithm (BFOA), Teaching Learning Based Optimization (TLBO), Jaya Algorithm (JA), Genetic Algorithm (GA) and Hybrid BFOA and Particle Swarm Optimization Algorithm (hBFOA-PSO) for the identical test systems has been demonstrated. The analysis was then extended to interconnected thermal power system of reheat type and two-area six-unit system. The results are compared with Firefly Algorithm (FA), Symbiotic Organism Search Algorithm (SOSA) and Artificial Bee colony (ABC) for second test system and TLBO, Hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS), ADE and hADE-PS tuned PID for third test system. Finally, robustness of the suggested controller is examined under varied conditions.

Generation of Addition Chain using Bacteria Foraging Optimization Algorithm

Generation of Addition Chain using Bacteria Foraging Optimization Algorithm

A hybrid GA-BFO algorithm for the profit-maximizing capacitated vehicle routing problem under uncertain paradigm

This study designs a new variant of the capacitated vehicle routing problem (CVRP) under a fuzzy environment. In CVRP, several vehicles start their journey from a central depot to provide services to different cities and finally return to the depot. This paper introduces an additional time beyond the service time at each city to fulfill the pre-ordered demands. The need for this excess service time is to provide the services to new customers who are not enlisted at the start of the process. It is a market enhancement step. The proposed model’s main objective is to find the maximum time-dependent profit by using the optimum number of vehicles in an appropriate route and spending optimum excess service time in each city. The model considers travel time and travel cost as fuzzy numbers. An expected value model (EVM) is formulated using the credibility approach on fuzzy variables. A hybrid meta-heuristic method combining a genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) is designed to solve the proposed model. The proposed model is explained with the help of some numerical examples. Sensitivity analyses based on different independent parameters of the algorithms are also conducted.

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.

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory.

In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization algorithm (IBFOA) and an improved intrinsic Tau jerk (named Tau-J*) guidance strategy. Besides, the adaptive factor and elite-preservation strategy are employed to facilitate the IBFOA, and an improved Tau-J* with higher-order of intrinsic guidance movement is used to avoid the nonzero initial and final jerk, so as to overcome the computational burden and unsmooth trajectory problems existing in the optimization algorithm and traditional interpolation algorithm. The IBFOA is utilized to determine a small set of optimal control points, and Tau-J* is then invoked to generate smooth trajectories between the control points. Finally, the results of simulation tests demonstrate the eminent stability, optimality, and rapidity capability of the proposed bio-inspired trajectory planning method.

Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.