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

QUORUM SENSING BASED BACTERIAL SWARM OPTIMIZATION ON TEST BENCHMARK FUNCTIONS

The Bacterial swarm optimization is one of the latest optimization technique mainly inspired from the swarm of bacteria. This paper introduces an intelligent Quorum sensing based Bacterial Swarm Optimization (QBSO) technique for testing and validation. The quorum sensing senses the best position of the bacteria by knowing the worst place in search space. By knowing these positions, the best optimal solution is attained. Here in this proposed QBSO algorithm the exploration capability of the bacteria is well improved. The proposed technique is validated on the seven standard benchmark with unimodal and multimodal test function for its feasibility and optimality. The basic swarm based optimization algorithms such as Particle Swarm Optimization, Ant Colony Optimization, Biogeography Based Optimization, Simulated Bee Colony and conventional Bacterial Swarm Optimization with the standard parameters are simulated and associated with the proposed technique. The attained results evidently indicate that the proposed method outperforms from the considered optimization methods. Further, the proposed technique may apply to any engineering problems, especially for complex real time optimization problems

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

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions

A Hybrid Bacterial Swarming Methodology for Job Shop Scheduling Environment

Optimized utilization of resources is the need of the hour in any manufacturing system. A properly planned schedule is often required to facilitate optimization. This makes scheduling a significant phase in any manufacturing scenario. The Job Shop Scheduling Problem is an operation sequencing problem on multiple machines with some operation and machine precedence constraints, aimed to find the best sequence of operations on each machine in order to optimize a set of objectives. Bacterial Foraging algorithm is a relatively new biologically inspired optimization technique proposed based on the foraging behaviour of E.coli bacteria. Harmony Search is a phenomenon mimicking algorithm devised by the improvisation process of musicians. In this research paper, Harmony Search is hybridized with bacterial foraging to improve its scheduling strategies. A proposed Harmony Bacterial Swarming Algorithm is developed and tested with benchmark Job Shop instances. Computational results have clearly shown the competence of our method in obtaining the best schedule

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

This paper presents the performance of utilizing a bacterial foraging optimization algorithm on a PID control scheme for controlling a five DOF two-wheeled robotic machine with two-directional handling mechanism. The system under investigation provides solutions for industrial robotic applications that require a limited-space working environment. The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink(®) environment. Bacterial foraging-optimized PID control with decoupled nature is designed and implemented. Various working scenarios with multiple initial conditions are used to test the robustness and the system performance. Simulation results revealed the effectiveness of the bacterial foraging-optimized PID control method in improving the system performance compared to the PID control scheme

Energy Efficient Communication Protocols for Wireless Sensor Networks

The popularity of Wireless Sensor Networks have increased tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since these devices rely on battery power and may be placed in hostile environments replacing them becomes a tedious task. Thus, improving the energy of these networks becomes important.The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency. It presents a comparison between the different methods on the basis of the network lifetime . It proposes a modified approach for cluster head selection with good performance and reduced computational complexity .In addition it also proposes BFO as an algorithm for clustering of WSN which would result improved performance with faster convergence