Nurhan Karaboga

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A new design method based on artificial bee colony algorithm for digital IIR filters

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.

Digital IIR filter design using differential evolution algorithm

Any digital signal processing algorithm or processor can be reasonably described as a digital filter. The main advantage of an infinite impulse response (IIR) filter is that it can provide a much better performance than the finite impulse response (FIR) filter having the same number of coefficients. However, they might have a multimodal error surface. Differential evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multimodal search space regardless of the initial parameter values, fast convergence, and using a few control parameters. In this work, DE algorithm has been applied to the design of digital IIR filters and its performance has been compared to that of a genetic algorithm.

Artificial bee colony programming for symbolic regression

Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as Artificial Bee Colony Programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.

Artificial immune algorithm for IIR filter design

Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms.

Designing digital IIR filters using ant colony optimisation algorithm

Abstract In order to transform and analyse signals that have been sampled from analogue sources, digital signal processing (DSP) algorithms are employed. The advantages of DSP are based on the fact that the performance of the applied algorithm is always predictable. There is no dependence on the tolerances of electrical components as in analogue systems. DSP algorithms can be reasonably described as a digital filter. Digital filters can be broadly divided into two-sub classes: finite impulse-response filters and infinite impulse-response (IIR) filters. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are required in order to avoid local minima and design efficient digital IIR filters. In this work, a new method based on the ant colony optimisation algorithm with global optimisation ability is proposed for digital IIR filter design. Simulation results show that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the proposed method can be efficiently used for digital IIR filter design.

Designing digital FIR filters using Tabu search algorithm

A novel method is presented for designing digital FIR filters. The method is based on a parallel Tabu search algorithm. The proposed approach has proved to be highly effective and produced a good solution to the design of a digital FIR linear phase filter.

Simple and accurate effective side length expression obtained by using a modified genetic algorithm for the resonant frequency of an equilateral triangular microstrip antenna

A new, very simple expression for the effective side length is presented for the resonant frequency of the equilateral triangular microstrip patch antenna. It is obtained by using a modified genetic algorithm, and useful to antenna engineers for accurately predicting the resonant frequencies of the equilateral triangular microstrip antennas. The theoretical resonant frequency results obtained by using this new effective side length expression are in very good agreement with the experimental results available in the literature.

Performance comparison of genetic and differential evolution algorithms for digital FIR filter design

Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum of a multi modal search space regardless of the initial parameter values, fast convergence, and using a few control parameters. DE algorithm which has been proposed particulary for numeric optimization problems is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of digital Finite Impulse Response filters and compared its performance to that of genetic algorithm.

A new effective patch radius expression obtained by using a modified tabu search algorithm for the resonant frequency of electrically thick circular microstrip antennae

A new, very simple expression for the effective patch radius is presented for the resonant frequency of electrically thick circular microstrip patch antennae. It is obtained by using a modified tabu search algorithm, and is useful for the computer-aided design (CAD) of microstrip antennae. The theoretical resonant frequency results obtained by using this new effective patch radius expression are in very good agreement with the experimental results available in the literature.