Bacterial Foraging Optimization Algorithm Research Articles

Optimized Deep Neural Network Based Load Balancing in Fog Computing with Robust Dynamic Scheduling Algorithm

The load balancing (LB) in FOG architecture is a formidable task with the availability of minimal resources. This field encounters enormous studies that portray the routing of task between the fog physical devices and cloud nodes. The presence of different types of heterogeneity devices are at helm of difficult scheduling process. To allocate LB based on the available resource we propose a novel Deep Neural Network (DNN) based Tuna swarm strategy based Bacterial Foraging optimization algorithm (TBFO) which employs three stages such as monitoring the fog resources, Classification based on deep learning technique and dynamic scheduler that are optimized. With the dynamic scheduling algorithm this work aims to provide LB in real time application. The first stage is to monitor the resources of server and amassed in fog resource table. The next step is to identify the unerring server and is effectuated with the proposed DNN technique. The last stage is to allocate the process to the selected server with the TBFO. It certifies the robust continuous services in the fog LB. Simulations are conducted and compared the outcomes with the existing works and ensures effective load balancing, make span and resource utilization.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System

This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects. 

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with a reduction range of 16.7–18.6%. Meanwhile, the charging and discharging loads were effectively reduced, with a reduction range of 16.2–19.7%. Therefore, this method has significant optimization effects on actual power grid operation. This research achievement contributes to the further development of dynamic reconstruction technology for distribution networks, improving the operational efficiency and stability of the power grid. This has important practical significance for achieving green and intelligent operation of the power system.

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

Phase Retrieval for Radar Constant–Modulus Signal Design Based on the Bacterial Foraging Optimization Algorithm

Optimizing the energy spectrum density (ESD) of a transmitted waveform can improve radar performance. The design of a time–domain constant–modulus signal corresponding to the transmitted waveform ESD is practically important because constant–modulus signals can maximize transmission power and meet the hardware requirements of radar transmitters. Here, we present a time–domain signal design under dual constraints of energy and constant modulus. The mutual information (MI)–based waveform design method is used to design transmitted waveform ESD under the energy constraint. Then, the bacterial foraging optimization algorithm (BFOA) is proposed to design the time–domain constant–modulus signal. We use minimum mean square error (MMSE) in the frequency domain as the cost function. The BFOA monotonously decreases the MMSE with increasing iterations, which makes the ESD of the time–domain constant–modulus signal close to the MI–based optimal waveform ESD. The simulation results indicate that the proposed algorithm has advantages, including insensitivity to initial phases, rapid convergence, smaller MI loss, and MMSE compared with the iterative reconstruction algorithm.

An efficient reconfigurable FIR filter design with coefficient optimization using a modified bacterial foraging optimization algorithm

ABSTRACT The digital filters play a significant role in the field of digital signal processing (DSP). The finite impulse response (FIR) filter is an attractive choice because of the ease of design and good stability. The digital filters have a wide variety of applications such as signal processing, control systems, telecommunication, etc. They are better than the analogue filters due to their performance. In recent times, software radios have achieved attention owing to requirements for integrated and reconfigurable communication systems. Hence, reconfigurations have emerged as a significant problem in the designs of FIRs. To match the frequencies of DSP applications, higher-order FIRs are required. If length of filters rises, addition and multiplication operations also increase. This paper proposes an efficient hardware design of RFIR that employs modified bacterial foraging optimizations (MBFOs) and common sub-expression eliminations (CSEs) in its executions. MBFOs output restricted counts of filter coefficients with sums of signed-power-of-two (SPT) terms while maintaining the quality of filtered responses. On obtaining coefficients, eliminations are executed by CSEs where hardware complexities are determined in terms of adders. Model sim software validated RFIRs using the Verilog code. The proposed design of RFIRs was compared with existing designs in terms of power usages, frequencies and areas.

Maximum Torque per Ampere Technique for Switched Reluctance Motor Drive

Numerous researchers have investigated switched reluctance motors (SRMs) as a competitor to other electrical motors for usage in electric and hybrid vehicle appliances. SRMs are fault-tolerant and have a broad operating speed range. However, they have numerous drawbacks, such as high nonlinearity in machine models, complex control, and torque ripple. This study aims to improve the SRM drive system to attain maximum torque per ampere in a wide speed range. In the design and control of SRMs, maximizing torque per ampere (MTPA) is a crucial objective. MTPA refers to the strategy of achieving the maximum possible torque output for a given current input (amperes). This is essential for enhancing the efficiency and performance of the motor. The paper focuses on achieving MTPA in an SRM drive system-based commutation angle control. Utilizing a bacterial foraging optimization algorithm (BFOA), the suitable commutation angles at each operation point are determined. The intended technique is straightforward to realize and does not require complex algorithms. Simulation comparisons on an 8/6 SRM are also presented to validate the intended control technique. The results indicate that the MTPA has been enhanced by 6.961% compared to the conventional method.

Robust integration of fault estimation and sliding mode fault‐tolerant control for interconnected systems against sensor fault

AbstractThis paper provided a robust strategy for controlling nonlinear interconnected large‐scale systems subject to a sensor fault. The inherent challenge in controlling such systems is to sustain robust closed‐loop stability and performance in nonlinear interactions, faults, and exogenous inputs. A new closed‐loop structure has been devised to tackle this challenge by integrating the sliding mode controller with the unknown input observer. By exploiting the decoupling capability of the controller and observer, this integration ensures the robustness of the closed‐loop system against the simultaneous effect of interaction, fault, and disturbance. In this context, the unknown input observer has been constructed to supply the controller with an accurate sensor fault assessment despite additional unknown inputs. A novel approach is proposed for designing a decentralized fault‐tolerant control system that utilizes sliding mode control and proportional‐integral‐derivative controller tuning via a bacterial foraging optimization algorithm to compensate for the fault effect, leading to a robust output performance. The observer and controller gains are accomplished by utilizing the H∞ performance and linear matrix inequality formulation. The Lyapunov technique is used to demonstrate stability. A power system model emphasizes the proposed approach's robustness and effectiveness. The system performance with and without the proposed controller was compared, where the simulation results show a fast reaction to offset undesirable impacts, whereas the state estimation error approaches to zero in each subsystem.

An Improved Adaptive Spiral Dynamic Algorithm for Global Optimization

This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF) algorithm is designed so that the chemotaxis phase of bacteria represents the exploration part of the search operation. In contrast, the SDA represents the exploitation part. Additionally, to improve search operation efficiency, the spiral model's radius and angular displacement are adaptively set according to a linear correlation concerning the fitness value. An additional phase, the elimination and dispersal phase, is obtained from BFA and added to the end of the SDA. This phase aims to improve the algorithm's final solution's accuracy by enhancing the algorithm's search strategy and performance. Simulation tests are run on unimodal and multimodal standard benchmark functions to verify the proposed algorithm. The proposed algorithm significantly outperforms SDA and Adaptive SDA (ASDA) algorithms regarding fitness value and accuracy.  

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).

Enhancing Controller Efficiency in Hybrid Power System Using Interval Type 3 Fuzzy Controller with Bacterial Foraging Optimization Algorithm

Microgrids (MGs) are designed with the help of effective power extracted from renewable sources such as rooftop solar panels, photovoltaic cells, batteries, floating PV and solar PV with the grid. In a hybrid microgrid, Interlinking Converter (ILC) is a key component to connect the AC sub-grid and DC sub-grid. DC-DC converters are being used as power converters in between load and source to enforce and increase the PV depending on the voltage output signal. Accordingly, the work focused on a Multi-Input (MI) KY boost converter. This Proposed topology gathered maximum power using multi-input KY boost converters for hybrid energy. This hybrid topology operates mainly delivered power from renewable energy sources solar/wind to dc bus. In the absence of any one source, wind or solar supplies power to the dc bus. Without any renewable energy, sources battery deliver the power to the dc bus. The research proposed the interval type 3 fuzzy controller is used for controlling the load frequency of the multi-area system. Swarm-based hybrid metaheuristic optimizer of the Bacterial Foraging Optimization Algorithm (BFOA) is proposed for optimal tuning and controlling the PI controller parameters. Controlling the reactive power of the hybrid power system model with the aid of a Static Synchronous Compensator (STATCOM). A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. In this paper effectiveness of the hybrid power system is simulated through MATLAB/SIMULINK. The battery current and voltage of this produce 2000 A and 205 V, grid voltage produced in this work is 1.9 × 104 V and the power of the work produce approximately 90 kW. The results show that the interlink converter improves the flexibility of the hybrid microgrid and, in addition, the power quality of the energy supplied in the utility grid is improved. In future, an intelligent control algorithm may be presented to improve the control strategies of the HMG, respectively.

Optimal gait planning of bionic lower extremity exoskeleton based on elite mechanism bacterial foraging optimization algorithm

To improve the stability and smoothness of bionic lower extremity exoskeleton walking gait, the elitist mechanism was introduced to enhance the diversity and improve the search ability of the optimal solution. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved foraging optimization algorithm was used for the optimization of the bionic lower extremity exoskeleton gait zero moment point stability margin. The simulation results showed that the optimal gait planning method proposed can increase the stability margin and ensure the stable and smooth walking.

Optimal selection of cloud manufacturing resources based on bacteria foraging optimization

ABSTRACT As a service-oriented manufacturing paradigm, cloud manufacturing (CMfg) combines the computing resources the manufacturing resources respectively from the Internet and enterprises into a huge shared resource pool for network-based distribution of manufacturing resources and manufacturing tasks. In order to reduce the number and complexity of resource pools in CMfg, it is necessary to simplify the process, save manufacturing resources, shorten the production time and improve the productivity of enterprises. Therefore, a method for the optimal selection of cloud Manufacturing resources based on bacterial foraging optimization was proposed in this paper. First, a scheme evaluation system based on the evaluation factors of production cost, production time and processing quality was established, and a mathematical model of resource optimization selection was built. Secondly, a combination of Analytic Hierarchy Process (AHP) and entropy weight method was used to obtain the weights of different evaluation indicators, and a bacterial foraging optimization algorithm (BFO) was used to optimize the scheme. Finally, an example study of the method was conducted with the NGW51 reducer as an example. By comparing with existing methods, the experimental results verify the advantages of BFO in CMfg resource optimization selection.

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.

Trajectory planning of a dual-arm space robot for target capturing with minimizing base disturbance

During the on-orbit capture task of the free-floating space robot, its operating space degenerates. The dynamic coupling and dynamic singularity also affect the task operation. In this paper, a general collision-free trajectory planning method to minimize base disturbance is proposed for a free-floating dual-arm space robot. This method can realize smooth joint trajectory planning while avoiding singularity and realizing self-collision avoidance. The singularity can be effectively avoided by transforming the trajectory planning problem into a nonlinear optimization problem. The optimization function is composed of base disturbance penalty function, end effector error function and collision repulsion potential field function, which can be used to solve the optimal joint trajectory parameters. Futhermore, self-collision avoidance is achieved by combining the bacterial foraging optimization algorithm(BFOA) with the geometric vector collision-avoidance method based on artificial potential field to simplify the calculation complexity. Finally, simulations verify the effectiveness of the proposed planning framework.

Maiden application of colliding bodies optimizer for load frequency control of two‐area nonreheated thermal and hydrothermal power systems

AbstractThis article presents a novel load frequency control (LFC) approach using colliding bodies optimizer (CBO) for frequency stabilization of interlinked multiarea electric power systems. The optimal parameters of the suggested CBO‐based proportional–integral–derivative‐filter controller ascertain an effective LFC solution. First, a well‐known and widely used linearized two‐area nonreheated thermal power system is examined to demonstrate the efficacy of the proposed approach. The effectiveness of the proposed method is analyzed by comparing the outcomes of several recently presented LFC schemes. The performance analysis shows a settling time improvement of 3.10%, 14.29%, and 18.66% in the case of area‐1 and area‐2 frequencies and tie‐line power deviations compared with an imperialist competitive algorithm (ICA)‐based controller. The robustness of the proposed scheme is also evaluated in the presence of various operating scenarios. Additionally, the work is extended to a two‐area nonreheated hydrothermal power system. The proposed method shows an improvement of over 60% in the performance index compared with several existing techniques‐based controllers such as optics‐inspired optimization, gray‐wolf optimization, quasi‐oppositional differential search algorithm, bacterial foraging optimization algorithm, and ICA.

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

In wireless networks, multiple monitoring nodes are used to collect users’ transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to optimize and schedule the channel resources of each node to cover more users, obtain more network data, and maximize the quality of network monitoring. In this paper, a channel allocation algorithm based on swarm intelligence—“discrete bacterial foraging optimization”—is proposed based on the classic bacterial foraging optimization algorithm. The position of each dimension in the iterative process is discretized to binary 0 or 1 to encode and express the channel allocation problem of wireless monitoring networks, and the channel allocation scheme is optimized by location updates guided by bacterial foraging. Many simulation and practical experiments have proved the effectiveness of the algorithm, and it also has low complexity and provable convergence. Compared with similar algorithms, this algorithm improves monitoring quality by 1.428% while boosting speed by up to 32.602%. The algorithm has lower complexity, higher performance, and can converge to the optimal solution at a faster rate.

Ship routing optimization using bacterial foraging optimization algorithm for safety and efficient navigation

Efficient operation plays a vital role to develop a sustainable shipping fleet with cost competitive. The requirements for economic efficiency, energy efficiency, reducing emissions, and increasing safety and security lead to an innovative model in the optimal weather routing system. The vessel routing is influenced by the quality of meteorological and oceanographic data such as wind, waves, and currents. In this study, the model optimization of weather routing considers the meteorological and oceanographic information, ship's characteristics combined with an adaptive bacterial foraging optimization algorithm (BFOA) will be introduced and applied to the ship’ navigation at sea. The simulation results will be evaluated the effectiveness and reliability of the model. This model will support ships’ navigation to be safer and more comfortable, operate more efficiently and reduce emissions.

Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.