Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality

Diseño de filtros digitales IIR mediante algoritmos meméticos híbridos tipo SFLA

Este Trabajo Fin de Grado desarrolla la aplicación de un algoritmo memético (MA) a la resolución de un importante problema de optimización, concretamente el diseño de filtros digitales de respuesta al impulso de longitud infinita, o IIR. Para ello se propone un método de optimización basado en un algoritmo memético del tipo Shuffled Frog Leaping Algorithm (SFLA). SFLA es un algoritmo memético basado en una población formada por ranas que saltan en búsqueda de comida, de forma que aquellas en una peor situación serán capaces de imitar a las más aventajadas saltando en su misma dirección. El conjunto de ranas o población se divide en subgrupos de forma que estos evolucionen de forma independiente para luego ser barajadas, intercambiando la información sobre posiciones beneficiosas que han sido obtenidas al saltar. Aunque la base del MA propuesto es el algoritmo SFLA desarrollado por Eusuff.M, Lansey.K y Pasha.F. en [13], se han introducido una serie de modificaciones que lo hacen aplicable a problemas multiobjetivo además de mejorar la resistencia ante la tendencia a converger hacia mínimos locales, algo frecuente en algoritmos genéticos (AG) mal parametrizados. En este aspecto se ha introducido un mecanismo novedoso para asegurar la diversidad aplicando la entropía de Shannon a la distancia entre individuos en el plano de funciones objetivo. Los resultados obtenidos en el diseño de filtros IIR se han comparado con los correspondientes al método de diseño clásico basado en la Transformada Z Bilineal, además de los presentados en el trabajo de Upadhyay.P, Kar.R, Mandal.D y Ghoshal.S.P [24]. Los resultados obtenidos son prometedores desde el punto de vista del rizado en la banda de paso y eliminada además de la respuesta en fase del filtro en la banda de paso, presentando una mayor carga computacional que el método clásico de la BZT, compensado con una mejora en sus características, reduciendo el MSE obtenido con respecto al filtro ideal entre un 117% y un 342%. Adicionalmente, para el caso de diseño de filtros FIR también se encontró una mejora, en concreto el MSE quedó reducido entre un 147% y un 192%.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

An Approximate Algorithm Combining P Systems and Ant Colony Optimization for Traveling Salesman Problems

This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman prob- lems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structure and transformation/communication rules of P systems. First, the parameter setting of the ACOPS is discussed. Second, extensive experiments and statistical analysis are investigated. It is shown that the ACOPS is superior to Nishida's algorithms and its counterpart ant colony optimization algorithms, in terms of the quality of solutions and the number of function evaluations