Bacteria Foraging Algorithm for Metamaterial Design and Optimization

Soft computing techniques are emerging as highly efficient global optimization techniques in the field of electromagnetics. These techniques along with the EM software have proved their efficiency in antenna engineering, wireless communication, absorber design and a few in the field of metamaterial structural analysis. Bacteria foraging algorithm, although has been used recently in controls, is still new to the field of metamaterial science and technology. In this paper, bacteria foraging algorithm (BFA) is used for design optimization of a double ring circular split ring resonator. Equivalent circuit analysis is used the EM tool for analysis of the CSRR. The aim of bacteria foraging algorithm is the estimation of structural parameters of the CSRR at a desired frequency range. Further the developed algorithm is proved through extraction of parameters of the optimized metamaterial structure. A comparative study with other soft computing techniques w.r.t. accuracy and computational time is provided

Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery over Emerging Mobile Computing Systems

An optimal synthesis of a wideband Log-Periodic Dipole Array (LPDA) is introduced in the present study. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio and the side lobe level, over a wide frequency range. The LPDA geometry that complies with the above requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called Invasive Weed Optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO method has been chosen to optimize an LPDA for operation in the frequency range 800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems

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

AbstractTwo 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

IWO-based Synthesis of Log-Periodic Dipole Array

The Invasive Weed Optimization (IWO) is an effective evolutionary and recently developed method. Due to its better performance in comparison to other well-known optimization methods, IWO has been chosen to solve many complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO is applied to optimize the geometry of a realistic log-periodic dipole array (LPDA) that operates in the frequency range 800-3300 MHz and therefore is suitable for signal reception from several RF services. The optimization is applied under specific requirements, concerning the standing wave ratio, the forward gain, the gain flatness and the side lobe level, over a wide frequency range. The optimization variables are the lengths and the radii of the dipoles, the distances between them, and the characteristic impedance of the transmission line that connects the dipoles. The optimized LPDA seems to be superior compared to the antenna derived from the practical design procedure

Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance

Curvelet transform and Hybrid Bacterial Foraging Optimization for image denoising

Eliminating noise from the original image is still a challenging task for researchers. Several algorithms have been proposed and each of them has its own assumptions, advantages & limitations. The paper proposes the noise reduction method for the medical images by using Hybrid BFO i.e the fusion of BFO (Bacteria foraging optimization) and the technique of contourlet transform and the results are compared with the older technique of image denoising using curvelet transform. BFO algorithm is an artificial intelligence nature-inspired optimization algorithm technique which is based on mimicking the foraging behavior of E.coli bacteria and it is now applied to the field of imagingdenosin

PID CONTROLLER TUNING OF 3-PHASE SEPARATOR IN OIL & GAS INDUSTRY USING BACTERIA FORAGING OPTIMIZATION ALGORITHM

In oil and gas industry, one of the most important stages in processing petroleum is separation. It can be classified by operating configuration such as vertical, horizontal and spherical or by its function which is 2-phase or 3-phase. In this paper, vertical 3-phase separator will be chosen and researched. 3-phase separator is used to separate water, oil and gas. Gas will be at the top, oil will be the middle layer and water will be at the bottom due to gravitational force and the density of the substance. The objective is to tune the PID controller controlling the level of the water in the separator. Outflow rate of the water from the bottom of the separator will be used to control the water level. Currently there are controlling methods namely PI control using trial and error method, PI control using Butterworth filter design method and IMC method. These methods were having quite high % overshoot and long settling time. So, this paper will introduce Bacterial Foraging Optimization Algorithm (BFOA) in optimizing the parameters for PI control. BFOA mimics the behaviour of the bacteria in searching for highest food concentration which then modified to search the best parameters for the PID controller. BFOA will be able to find the best parameters compared with the conventional methods and show better performance than PI control using trial and error method, PI control using Butterworth filter design method or IMC method. BFOA will be studied and other existing conventional methods as well. Simulation will be done based on the mathematical model of the 3-phase separator

The Application of Improved Bacteria Foraging Algorithm to the Optimization of Aviation Equipment Maintenance Scheduling

Taking the aviation equipment scheduled maintenance as a prototype, this paper improves a bionic global random search algorithm - bacteria foraging optimization algorithm to solve the task-scheduling problem. Inspired by gene mutation, the activity of bacteria is dynamically adjusted to make good bacteria more capable of action. In addition, a bacterial quorum sensing mechanism is established, which allows bacteria to guide their swimming routes by using their peer experience and enhance their global search capability. Its application to the engineering practice can optimize the scheduling of the maintenance process. It is of great application value in increasing the aviation equipment maintenance efficiency and the level of command automation. In addition, it can improve the resource utilization ratio to reduce the maintenance support cost

Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

AbstractThe medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG). A Bundle Branch Block (BBB) is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO) Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important) feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN) based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature

Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation

© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator