Bilal Babayigit

A clonal selection algorithm for null synthesizing of linear antenna arrays by amplitude control

This paper presents a clonal selection algorithm based on human immune system for null steering of linear antenna arrays by controlling only the element amplitudes. Design requirements such as the null depth level, maximum sidelobe level, and dynamic range ratio are considered in the optimization process. Several examples of Chebyshev pattern with the imposed single, multiple and broad nulls are given to show the accuracy and flexibility of the proposed clonal selection algorithm.

Clonal Selection Algorithm for Design of Reconfigurable Antenna Array with Discrete Phase Shifters

In this paper, a method based on the clonal selection algorithm (CLONALG) is presented to design a reconfigurable dual-beam linear antenna array with excitation distributions differing only in phase. The CLONALG is a relatively novel population-based evolutionary algorithm inspired by the clonal selection principle of the human immune system (IS). From the practical implementation viewpoints, the proposed method takes discrete phase shifters into account during synthesis. Numerical examples showing a good agreement between the desired pattern and the synthesized pattern by using CLONALG are given.

Interference suppression of linear antenna arrays by phase-only control using a clonal selection algorithm

In this paper, an efficient technique based on clonal selection algorithm (CLONALG) for linear antenna array pattern synthesis with null steering by controlling only the element excitation phases is presented. The CLONALG is an evolutionary computation method inspired by the clonal selection principle of human immune system. To show the versatility and flexibility of the proposed CLONALG, some examples of Chebyshev array pattern with the imposed single, multiple and broad nulls are given. The sensitivity of the nulling patterns due to small variations of the element phases is also investigated.

A modified artificial bee colony algorithm for numerical function optimization

Artificial bee colony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploitation. This paper presents a new modified ABC algorithm for numerical optimization problems to improve the exploitation capability of the ABC algorithm. A different probability function and a new searching mechanism are proposed. The modified ABC algorithm is tested on seven numerical optimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.

A CLONAL SELECTION ALGORITHM FOR ARRAY PATTERN NULLING BY CONTROLLING THE POSITIONS OF SELECTED ELEMENTS

In this paper, a method based on clonal selection algorithm (CLONALG) is proposed for null steering of linear antenna arrays by controlling only the positions of selected elements. The CLONALG is a relatively novel population-based evolutionary algorithm inspired by the clonal selection principle of the human immune system. In order to illustrate the accuracy and flexibility of the proposed algorithm, several numerical examples of Chebyshev pattern with the single and double nulls imposed at the directions of interference are given.

Design optimization of circular antenna arrays using Taguchi method

This paper presents the application of Taguchi method (TM) to design optimization of non-uniform circular antenna array (CAA) for suppression of sidelobe levels (SLLs). TM, a robust design approach, takes signal-to-noise ratio and orthogonal array tools from the statistical design of experiments. These tools allow instead of full factorial parametric analysis minimize the design parameters; thus, increase the convergence speed and generate more accurate solutions. TM is used to determine an optimal set of amplitudes and positions of CAA for 8, 10, and 12 elements. Comparison of the results of the TM with those of latest meta-heuristic algorithms in the literature reveals that the CAA design with TM provides the best SLL reduction performance in all cases.

An ABC algorithm with inversely proportional mutation

Artificial bee colony (ABC) algorithm has been very popular in the last few years in computational intelligence research area. This paper proposes a modified version of the ABC algorithm for solving numerical function optimization problems. The purpose of this modified ABC algorithm is to improve the exploitation capability of the ABC algorithm. For this purpose, the proposed ABC uses an inversely proportional mutation function and a new search mechanism. Experiments are performed on a set of 9 widely used benchmark problems. The experimental results show that this modified ABC algorithm outperforms the standard ABC algorithm.

Optimum broadband E-patch antenna design with Taguchi method

Abstract This paper describes the design of a broadband E-patch antenna using Taguchi method. Although this optimization method exhibits a fast convergence speed, its application to the antenna design has so far been very limited as compared to the particle swarm optimization and differential evolution. Different than the prior work on optimization-based design of E-patch antennas, the presented design is carried out by satisfying multiple objectives: the minimum antenna footprint area and |S11| < −10 dB bandwidth across 5–6 GHz band. The performance of this E-patch antenna design is compared with that of an E-patch antenna designed with the differential evolution algorithm to meet the same |S11| < −10 dB bandwidth criteria. It is shown that a two-level Taguchi method applied to E-patch design problem with 6 degrees of freedom can complete the design with 8 full-wave simulations as compared to the entire 26 = 64 possibilities. This is also considerably faster than the differential evolution algorithm that may require >300 full-wave simulations. In addition, it is shown that the presented design can meet the bandwidth criteria with a smaller footprint, hence demonstrates that minimization of the footprint must also be a part of the design process to achieve the best antenna solution. Furthermore, the E-patch antenna is fabricated and measured to confirm the validity of the proposed design.

Application of the Taguchi method to the design of circular antenna arrays

The main consideration in circular antenna array (CAA) design is to suppress the maximum sidelobe level (MSL). Also, lower dynamic range ratio (DRR) and smaller physical size (circumference) of CAA are preferable in some practical applications. In this paper MSL, DRR, and circumference of 10-element non-uniform CAA designs are examined using Taguchi method (TM). TM is a design optimization method and developed on the basis of the orthogonal array (OA) concept. TM can effectively explore the search space and select optimal values for design parameters. Optimal set of excitation amplitudes and element positions of CAAs for three different instances are determined by TM. The experimental results of TM show better performances compared to those of Genetic Algorithm, Particle Swarm Algorithm, standard ABC, and modified ABC algorithm.

Enhancing artificial bee colony algorithm using inversely proportional mutation

Artificial bee colony (ABC) algorithm is a recently invented powerful optimiser. ABC has become very popular in swarm intelligence research area and has the advantages of its few control parameters, simplicity and ease of implementation. However, latest studies have been devoted to the improvement of the exploitation capability of the standard ABC, because ABC is good at exploration but poor at exploitation, and the convergence speed is also an issue in some cases. Motivated by these issues, this paper proposes a modified ABC algorithm that uses an inversely proportional mutation function and a new search mechanism to solve numerical function optimisation problems. The proposed algorithm is applied to a set of nine well–known benchmarks with different dimensions. To verify the performance of the proposed algorithm, it is compared with the standard ABC algorithm. Experimental results demonstrate that the proposed modified ABC algorithm performs much better than the standard ABC algorithm.