Improved chemotaxis differential evolution optimization algorithm

The social foraging behavior of Escherichia coli has recently received great attention and it has been employed to solve complex search optimization problems.This paper presents a modified bacterial foraging optimization BFO algorithm, ICDEOA (Improved Chemotaxis Differential Evolution Optimization Algorithm), to cope with premature convergence of reproduction operator.In ICDEOA, reproduction operator of BFOA is replaced with probabilistic reposition operator to enhance the intensification and the diversification of the search space.ICDEOA was compared with state-of-the-art DE and non-DE variants on 7 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014). Simulation results of CEC 2014 benchmark functions reveal that ICDEOA performs better than that of competitors in terms of the quality of the final solution for high dimensional problems

Метод оптимизации на основе моделирования перемещения бактерий для решения задачи отбора информативных признаков при построении распознающих моделей

Решена задача автоматизации поиска наиболее информативной комбинации признаков. Предложен метод отбора информативных признаков на основе моделирования перемещения бактерий. Проведены эксперименты по выделению набора информативных признаков для синтеза распознающих моделей хронического бронхита.Решена задача автоматизации поиска наиболее информативной комбинации признаков. Предложен метод отбора информативных признаков на основе моделирования перемещения бактерий. Проведены эксперименты по выделению набора информативных признаков для синтеза распознающих моделей хронического бронхита.The automation of feature selection problem is solved. The feature selection method based on bacteria foraging optimization is proposed. Experiments on allocation of informative feature set for recognizing models of chronic bronchitis synthesis are lead

STUDY AND ANALYSIS OF STATISTICAL FEATURES OF FACE EXPRESSION IN NOISY ENVIRONMENT

This paper presents a new approach for the recognition of emotions in noisy environment. The approach presents the cascading of Wiener filter and Mutation based bacteria optimization technique (MBFO) to remove the noise from the highly corrupted face image .After removing the noise by the combination of wiener filter and MBFO technique and then detects the local , global and statistical feature form the image. Bacterial foraging optimization algorithm (BFOA) is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. In this research paper seven emotions namely anger, fear, happiness, surprise, sad, disgusting and neutral will be tested from database in noisy environment of speckle noise. facial expressions recognition system is based on a representation of the expression, learned from a training set of pre-selected meaningful features. However, in reality the noises that may embed into an image document will affect the performance of face recognition algorithms. Finally, emotion recognition will be performed by giving the extracted eye, lip and mouth blocks as inputs to a feed-forward neural network trained by back-propagation

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

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

Quantifying Losses in Power Systems Using Different Types of FACTS Controllers

This thesis discusses the placement of conventional power flow controllers (namely the Fixed series capacitor (FSC), Phase Angle Regulating Transformer (PAR)) and Flexible AC Transmission System (FACTS) devices (namely the Thyristor Controlled Series Capacitor (TCSC), the Static Synchronous Series Compensator (SSSC), the Unified Power Flow Controllers (UPFC) and the Sen Transformer (ST)) in bulk power systems to minimize transmission losses in the entire system. This firstly resolves line overloading and improves the overall voltage profile of the entire system. Secondly the transmission losses are minimized and also help in reducing the generation, which results in additional dollar savings in terms of the fuel costs. The sizes of the FACTS devices used were small in order to keep the initial installation costs low for the utility. The reduced FACTS device ratings are mentioned as a benefit, but not included in the overall loss minimization calculations. Various types of FACTS devices were modeled and placed in the power system, and the economic benefits were discussed and compared for different power flow conditions. The FSC, PAR, and TCSC are the FACTS Devices commonly used in the electric utility industry. In addition to the previous devices, the SSSC and UPFC were also modeled in the popular PSS/E and PSAT software's. The Sen Transformer was modeled using an electromagnetic transient simulation program (PSCAD/EMTDC). A line stability index was used to find the optimum location for placing the FACTS device. This thesis also provides a quantified value for the overall losses with the different FACTS devices, which is not available in the previous research literature. The Sen Transformer is a new type of a FACTS device that was developed by a former Westinghouse engineer, Dr. Kalyan Sen in 2003. It is based on the same operating principle as a UPFC (i.e. provides independent active and reactive power control) but uses the proven transformer technology instead. The benefit of the SEN transformer is that it would cost approximately only 30% of the UPFC cost. This thesis studies the Sen Transformer for loss minimization. Since the Sen technology uses a mature transformer technology, its maintenance costs are going to be less and therefore the utilities would be more comfortable using such a device instead of UPFC. A 12 bus test system proposed by FACTS modeling working group was used for validating and testing the FACTS devices in this thesis. This test system is a composite model of Manitoba Hydro, North Dakota, Minnesota, and Chicago area subsystems. This test platform manifests number of operating problems, which the electric utilities typically face. This system has been used for congestion management, voltage support and stability improvement studies with the FACTS devices. The results show that compensating a short transmission line in this system is more effective in minimizing the overall losses and improving the voltage profile compared to a typical approach of compensating long lines. The results also show that the UPFC and Sen Transformer are the most effective in minimizing the overall losses with the Sen Transformer being the most cost effective solution

Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and utilising the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upperfrequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which utilises Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and op-amp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all- pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. High-pass, low-pass, and all- pass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order op-amp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder- Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.Tertiary Education Trust Fund (TETFUND) through University of Calabar, Nigeria

A study on an integrated observation and collision avoiding support system for merchant ships

東京海洋大学博士学位論文 平成23年度(2011) 応用環境システム学 課程博士 甲第253号指導教員: 大津皓平全文公表年月日: 2016-12-13東京海洋大学201