Bacteria Foraging Algorithm Research Articles

A novel deep learning based forecasting model for carbon emissions trading: A comparative analysis of regional markets

This paper proposes a deep learning-based forecasting system for carbon emissions trading in regional markets. The system utilizes a hybrid convolutional neural network (CNN) deep learning algorithm to predict future carbon emission levels and facilitate the trading of carbon emission allowances. The system is compared to traditional methods of forecasting in order to assess the accuracy and performance of the system. In order to enhance the CNN performance, a new optimization algorithm based on bacteria foraging algorithm (BFA) is proposed which uses a modification to make a global search. By leveraging digital twins in the markets, a comparison is conducted using data from three regional carbon markets: the European Union Emissions Trading System, the Regional Greenhouse Gas Initiative, and the China Carbon Market. Results show that the proposed BFA-CNN based deep learning-based system outperforms traditional forecasting methods in terms of accuracy and provides more reliable estimates of future carbon emissions. The proposed system is a novel approach to carbon emissions trading and has the potential to improve the efficiency of regional carbon markets.

Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)

This paper presents comparative performance analysis of GAs, PSO and BFA technique-based AGC regulators with MSPPs in each system. Each control area consists of hydro, thermal and gas power plants (HTGPPs) for generation of electricity. In this work, proportional integral derivative (PID) control structure-based AGC regulators are designed by using the above intelligent techniques. However, the cost function for designing of these intelligent controllers is formulated using the integral square error (ISE) performance index. In the previous AGC studies, simulation time for tuning of above intelligent AGC controller to analyze performance was not reported. Therefore, this research work proposes a new study to investigate the performance of these intelligent AGC controllers based upon performance indicators: (i) simulation time of these techniques, (ii) first peak value of transient response, (iii) oscillations in transient response and (iv) settling time of transient response. The parameters of the controllers are evaluated using these techniques and investigations are carried out to find the best performance of the system. Besides, system dynamic performance of BFA-tuned AGC controllers are also demonstrated for +1% to +4% step load disturbance in the respective control areas.

Optimal control of a high gain DC- DC converter

<p class="IEEEAbtract">Different types of power conditioning systems are mostly used with renewable energy systems. As the output of solar photovoltaic (PV) is low high voltage converters are required. These converters can be used for both standalone and grid tied systems. A high voltage gain DC-DC converter is proposed which can be used with photovoltaic modules to get high output voltage. This converter can provide a high voltage gain and a proportional integral derivative (PID) controller is used to maintain the high output voltage of the proposed converter coupled with solar photovoltaic module. The PID controller is first designed using classical Ziegler Nichol’s method (ZN) and optimal control of the converter is obtained by using different optimisation methods like particle swarm optimisation (PSO), bacteria foraging algorithm (BFA) and its comparison is presented in this paper.</p>

Bacteria Foraging Algorithm for Optimal Topology Construction in Wireless Sensor Networks

Topology control is a significant method to reduce energy consumption and prolong the network lifetime. Connected Dominated Sets (CDS) are the emerging technologies to construct the energy- efficient optimal topology. Traditional topology construction algorithms are not utilized suitable optimization techniques for finding the optimum location of the active nodes in the networks. In this paper, Bacteria Foraging Algorithm (BFA) identifies the optimal location for active nodes to form the virtual backbone of the network. Residual energy and network connectivity are considered to evaluate the fitness function. The performance of the BFA is compared with other algorithms namely A3, A1, Genetic Algorithm (GA), and Gravitational Search Algorithm (GSA) algorithms for considering the performance metrics of the active nodes, residual energy, and connected sensing area coverage. Simulation results show that the proposed methodology performs well for reducing energy consumption and improving the connected sensing coverage area in the wireless sensor network.

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, and 6.6% less energy compared with SA, GA, PSO, BFA, ACO, BA, and GSP, respectively.

Energy Enhancement of Permanent Magnet Synchronous Generators Using Particle Swarm Optimization

Wind Energy Conversion Systems (WECS) are extensively used for connecting directly to grid sources. Permanent magnet synchronous generator (PMSG) based WECS is coupled to both grid and machine through converters. PMSG usually associated rectifiers with converters and voltage source converters at machine side. In this work, PMSG associated rectifiers with converters are considered for analysis of grid stability. The proposed work used Particle Swarm Optimization (PSO) based optimization methods for extraction of maximum power within boundary condition in WECS operation using PMSG. This high-tech optimization has MPPT controller for pitch angle controller (PAC) combines with PSO optimized controllers for converter and grid power converters. PSO produces noteworthy optimum coefficients ensuring competent MPPT operation in PAC. Extensive simulation work has been carried out with power converter for current controller of rotor and grid side current controller in MATLAB Simulink environment to assess the proposed system performance in terms output voltage, output current, maximum power extraction. Energy and efficiency of the system when the PMSG based WECS energy injected into the electric grid. To prove the efficiency of the proposed method, the performances have been compared with and Bacteria foraging Algorithm (BFOA). Subsequently, it is evident that proposed PSO based method subsidizes maximum efficiency of MPPT controller by extracting maximum power compared to other conventional techniques.

A Novel Gradient Based Optimizer for Solving Unit Commitment Problem

Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique.

Efficiency and economic optimization of shell and tube heat exchanger using bacteria foraging algorithm

Shell and tube heat exchanger (STHE) is widely used in the industries for various purposes due to its capacity to withstand pressure. STHE has the drawback of more cost and lower efficiency. Many existing methods involve in applying the optimization technique to decrease the cost of the system. Existing method has the lower efficiency in the optimization due to the random initialization and poor convergence. This research applies the bacteria foraging algorithm (BFA) to increase the efficiency and decrease the cost of the STHE system. The BFA has the advantages of the faster convergence and the global search approach to increase the performance in design. The proposed BFA is evaluated in the two case studies and compared with the existing method. The proposed BFA in multi-objective optimization of STHE has the total cost of 41,464 €, while existing method has the total cost of 41,913 €. The proposed BFA method also achieved high heat efficiency coefficient of 1089, and the existing method has 1031.47.

An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm

In this study, the workspace volume of a 3R manipulator has been maximized using a biologically inspired optimization algorithm, namely Adaptive Cuckoo Search (ACS) algorithm. The proposed algorithm is tested on four diverse cases involving different constraints and without imposing any constraint. The outcomes of this study are compared with the standard results of different heuristics, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), and Cuckoo Search (CS) algorithm. Statistical tests were performed to test the superiority of the proposed algorithm. Further, the cross-sectional area of the voids in the workspace has been estimated. The convergence study along with the coefficient of variation analysis identifies the critical kinematic parameters. A constraint conformation study has been performed to investigate the relative importance of the constraints. In addition, to ensure the applicability of the proposed algorithm in practice it is tested by using the kinematic parameters of two existing industrial robot manipulators (KUKA KR-30 and Mitsubishi MRP-700A). It has been found that the predicted results from the proposed algorithm for the two robot manipulators are in line with the actual values.

Arbitrary reconfiguration of universal silicon photonic circuits by bacteria foraging algorithm to achieve reconfigurable photonic digital-to-analog conversion

Reconfigurable/reprogrammable universal silicon photonic circuits represent a paradigm shift in designing photonic devices. However, it is very challenging to perform adaptive arbitrary reconfiguration when the high-dimensional solution of phase distribution cannot be explicitly determined, especially when there are random initial phase errors, which hinder the implementation of novel potential functions in universal circuits. This work presents an arbitrary black-box reconfiguration for universal circuits with random phase errors by a bacteria-foraging algorithm and unlocks a novel function of arbitrary-port-and-arbitrary-bit-resolution reconfigurable 6-bit photonic digital-to-analog conversion. This work offers a general and efficient method to ease multipurpose reconfiguration for universal silicon photonic circuits.

Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA

Electricity generation from the wind and solar photovoltaic (PV) systems are highly dependent upon weather conditions. Their intermittent nature leads to fluctuations in their output. Therefore, the need for rapid compensation for energy transmission and distribution systems is increasingly important. Static Synchronous Compensator (STATCOM) can be adopted for reactive power compensation and for decreasing the voltage fluctuation caused by the system and renewable energy sources. This study presents modelling of a Solar PV-Wind Hybrid Micro-grid and the increase of the stable operating limit of the system in case of the incorporation of STATCOM is examined. The major contribution of this paper is the optimization of gain parameters of four PI controllers in STATCOM control circuit based on genetic algorithms (GA) and Bacteria Foraging Algorithm (BFA) and therefore obtaining better responses and voltage stability in terms of nonlinear nature of solar-wind hybrid micro-grid. The Simulink models of the system architecture include a wind turbine model, a solar PV power system model and a STATCOM. It is certified that the voltage fluctuation at the end of the bus bar is reduced by 8% using conventional PI controller, by 10% for GA-based PI controller, and by 15% for BFA based PI controller under variable load. The results obtained by GA and BFA-based optimization of PI controllers are compared with that of the conventional controller and better results attained.

The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

Robust type-2 fuzzy fractional order PID controller for dynamic stability enhancement of power system having RES based microgrid penetration

The increased penetration of renewable energy sources (RES) based microgrid (MG) to the power system can affect the system stability due to the difference in dynamic characteristics of this kind of MG from the conventional generators. The reduction in system inertia caused by these power electronics interfaced renewable power generations introduces a negative effect in the dynamic stability of the system and may cause ensuing instability problems. On the other hand, power injection of RES based MG improves the damping performance with the reduction of transmission line congestion and power loss. Thus it becomes imperative to analyze the new dynamic stability concerns and control performance of a power system in the context of increased MG penetration. This study thoroughly investigates the impact of RES based MG penetration on the dynamic stability and control of a multi-machine multi-area system under varying operating conditions. The design of a novel type-2 fuzzy fractional order PID based power system stabilizer (PSS) using a meta-heuristic hybrid algorithm is presented in this paper for improving the electromechanical oscillation damping performance of the power system to enhance the dynamic stability. The large bandwidth, memory effect and flat phase contribution in the frequency response of fractional-order PID controllers are exploited to make the controller perform well against a wide range of system uncertainties in the presence of an MG. First, the parameter tuning problem of the type-2 fuzzy fractional order PID controller is transformed to an optimization problem that is solved using a hybrid algorithm by combining a dynamic genetic algorithm (DGA) with a bacteria foraging algorithm (BFA). The capability of the proposed controller in damping the low-frequency oscillations in the system with various MG penetration ratios is assessed through non-linear time-domain simulations and some performance indices under different disturbances and operating conditions.

The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

Bacteria Foraging Algorithm Based Optimal Multi Sink Placement in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSN) are mainly utilized for time sensitive applications such as forest fire detection systems and health monitoring systems. Sensor nodes are operated on low power and limited computation process. It is essential to develop the solution for planning the topological area. Multiple sinks are located in the network and reduce the number of hops between the sensors and its sinks. We propose an efficient technique based on Bacteria Foraging Algorithm to identify the best optimal locations of sinks. The experimental results show that average end to end delay is minimized and average energy consumption of sensor nodes are reduced.

Symbiotic organisms search algorithm based solution to optimize both real power loss and voltage stability limit of an electrical energy system

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

Performance evaluation of two popular antennas designed using a Bacteria Foraging Algorithm

Two popular antennas such as the Yagi-Uda Array (YUA) and the Log Periodic Dipole Array (LPDA) with the same number of dipole elements are optimally designed using Bacteria Foraging Algorithm (BFA). BFA being one of the successful optimization algorithms, used to optimize many design parameters of these two antennas to get a number of desired performance parameters. A YUA is designed here, mainly to realize high directivity, input-impedance (Zin) close to 50Ω, high Front To Back Ratio (FTBR), high Front-to-maximum-Side-Lobe-Level (FSLL), low Half Power Beam Width (HPBW), and appreciable bandwidth, whereas a LPDA is designed here, mainly to achieve high bandwidth, average Zin close to 50Ω, high average FTBR, high average FSLL, low average HPBW, and appreciable average directivity. The successful design approaches, application and comparative study of these two antennas presented here can also be extended to other antennas.

Water Cycle Algorithm for Small Scale Electrical Economic Power Dispatch Problem

Efficient and optimal planning of economic electrical power dispatch problem is an integral part of economic electrical energy generation planning and it is the need of time for the electrical engineers to browse this area in multi-scale planning scenarios. Intelligent and Optimization techniques based on evolutionary computing, metaheuristic,biological base,nature inspired, search method establish their applications in the area of electrical economic power dispatch planning(EEPDP) to reach global optimal solution for this multi scale, multi-decision, multi-objective combinatorial problem subjected to different constraints. In this paper, water cycle algorithm (WCA) has been proposed to solve electrical economic power dispatch problem for three and six unit system.This is based on how the streams and rivers flow downhill towards the sea and recycle in nature The suggested technique is tested on small scale system of three and six unit system of EEPDP considering various equality and inequality constraints . Test results are compared with other techniques grey wolf optimization(GWO),cuckoo search(CS),artificial bee colony(ABC),firefly algorithm(FA),particle swarm optimization(PSO),shuffled frog leap (SFL) ,bacteria foraging algorithm(BFO),harmony search(HS) applied in literature.Convergence of solution with iteration is presented for both cases.Simulation results proved that the WCA technique is better as compared to other nature inspired,heuristic,metaheuristic techniques to find global minima and maintain the solution quality in terms of low fuel cost.

Ant Lion Optimizer based Approach for Optimal Scheduling of Thermal Units for Small Scale Electrical Economic Power Dispatch Problem

A novel nature inspired algorithm ant lion optimizer (ALO) is recently developed which is motivated from the hunting mechanism of ant lions .Inherit steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building are simulated to find the optimal solution of real life problems . Intelligent and Optimization techniques based on evolutionary computing, metaheuristic,biological base,nature inspired, search method establish their applications in the area of electrical economic power dispatch planning(EEPDP) to reach global optimal solution for this multi scale, multi-decision, multi-objective combinatorial problem subjected to different constraints.An application of ALO to solve non linear electric economic power dispatch problem(EEPDP) is proposed in this paper. Efficient and optimal planning of economic electrical power dispatch problem is an integral part of economic electrical energy generation planning and it is the need of time for the electrical engineers to browse this area in multi-scale planning scenarios.. The performance of s ant lion optimizer (ALO) to solve electrical economic power dispatch problem is tested on three and six unit system.Test results are compared with other techniques grey wolf optimization(GWO),cuckoo search(CS),artificial bee colony(ABC),firefly algorithm(FA),particle swarm optimization(PSO),shuffled frog leap (SFL) ,bacteria foraging algorithm(BFO),harmony search(HS) applied in literature. Simulation results proved that the ALO technique is better as compared to other nature inspired,heuristic,metaheuristic techniques to find global minima and maintain the solution quality in terms of low fuel cost.

Bacteria Foraging Algorithm in Antenna Design

A simple design procedure to realize an optimum antenna using bacteria foraging algorithm (BFA) is proposed in this paper. The first antenna considered is imaginary. This antenna is optimized using the BFA along with a suitable fitness function formulated by considering some performance parameters and their best values. To justify the optimum design approach, one 12-element Yagi-Uda antenna is considered for an experiment. The optimized result of this antenna obtained using the optimization algorithm is compared with nonoptimized (conventional) result of the same antenna to appreciate the importance of optimization.