Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure

Assisted-Control Strategies On Electric Vehicles In Order To Achieve Optimal Energy Efficiency

Los vehículos eléctricos (EVs) están ganando popularidad, y las razones detrás de esto son muchas. La más destacada es su contribución a la reducción de las emisiones de gases de efecto invernadero. Se espera que los vehículos eléctricos, con suficiente penetración en el sector del transporte, reduzcan esos indicadores de emisiones. Como vehículo, un EV es silencioso, fácil de operar y no tiene los costos de combustible asociados con los vehículos convencionales. Como modo de transporte urbano, es beneficioso. No utiliza energía ni emisiones mientras está en ralentí, es capaz de conducir con paradas y arranques frecuentes, proporciona par completo desde el principio. El par instantáneo lo hace muy preferible para los deportes de motor. También se está desarrollando la red eléctrica de próxima generación, denominada "red inteligente". Los vehículos eléctricos son vistos como un contribuyente significativo a este nuevo sistema energético compuesto por instalaciones de generación renovable y sistemas de red avanzados. Todo esto ha llevado a un renovado interés y desarrollo en este modo de transporte. Esta tesis doctoral se centra en la propuesta de estrategias para mejorar la eficiencia energética de los vehículos eléctricos mediante un control asistido óptimo. Para generar una descripción detallada del vehículo, se realizan ensayos experimentales en ruta y en laboratorio, utilizando un banco dinamométrico y combinándolo con el modelo matemático de la dinámica del vehículo. La estrategia desarrollada muestra que la eficiencia energética en la conducción puede aumentar entre un 2% y un 3% en función del estilo de conducción. Por otro lado, para el sistema de frenado regenerativo se ha propuesto una estrategia óptima de control asistido basada en conseguir una mejora en la recuperación de energía de hasta un 8%. Estos resultados permitirán el inicio de trabajos futuros centrados en la implementación de sistemas asistidos para vehículos eléctricos actuales y propuestas de optimización energética para vehículos autónomos.Electric vehicles (EVs) are capturing popularity, and the reasons behind this are many. The most outstanding is its contribution to the reduction of greenhouse gas emissions. Electric vehicles, with sufficient penetration in the transport sector, are expected to reduce those emission indicators. The EVs are quiet, easy to operate, and the average cost : EV is 3 times cheaper than fuel internal combustion engine (ICE). As a mode of urban transport, it is beneficial. It uses no energy or emissions of harmful chemicals, gases and particle pollution while idling. EV is capable of frequent stop-and-go driving using minimal power and provides full torque right from the start and the instant torque makes it highly preferable for motorsports. The next-generation power grid, called the "smart grid," is also being developed. Electric vehicles are seen as a significant contributor to this new energy system made up of renewable generation facilities and advanced grid systems. All this has led to a renewed interest and development in this mode of transport. This doctoral thesis focuses on the proposal of strategies to improve the energy efficiency of electric vehicles through optimal assisted control. In order to generate a detailed description of the vehicle, experimental tests are accomplished on routes and in the laboratory, using a dynamometric bench and combining it with the mathematical model of the vehicle’s dynamics. The developed strategy shows that driving energy efficiency can increase between 2% and 3% depending on the driving style. On the other hand, for the regenerative braking system, an optimal assisted control strategy has been proposed based on achieving an improvement in energy recovery of up to 8%. These results will allow the start of future work focusing on implementing assisted systems for current electric vehicles and proposals for energy optimization for autonomous vehicles.Doctor en IngenieríaDoctorado0000-0002-3506-2522https://scholar.google.com/citations?user=O9MNXNMAAAAJ&hl=e

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field