From the Publisher:
This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. 

Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

Tabu Search

Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

Optimum design of hybrid renewable energy systems: Overview of different approaches

Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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A survey: algorithms simulating bee swarm intelligence

Resonance conditions for a substrate-superstrate printed antenna geometry which allow for large antenna gain are presented. Asymptotic formulas for gain, beamwidth, and bandwidth are given, and the bandwidth limitation of the method is discussed. The method is extended to produce narrow patterns about the horizon, and directive patterns at two different angles.

Gain enhancement methods for printed circuit antennas

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

An efficient method based on bees algorithm (BA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling only the amplitude of each array element. Numerical examples of Chebyshev pattern with the single, multiple and broad nulls imposed at the directions of interference are given to show the accuracy and flexibility of the BA.

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Amplitude-Only Pattern Nulling of Linear Antenna Arrays with the Use of Bees Algorithm

A useful and flexible method based on the tabu search algorithm for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling the amplitude-only and both the amplitude and phase of each array element. To show the versatility of the present method, some design specifications such as the sidelobe level, the null depth and the dynamic range ratio are considered by introducing a set of weighting factors in the cost function constructed for the tabu search algorithm. Several illustrative examples of Chebyshev pattern with the imposed single, multiple and broad nulls are given.

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Null steering of linear antenna arrays using a modified tabu search algorithm

A method for calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas, based on the backpropagation multilayered-perceptron network, is presented. The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided design (CAD) of microstrip antennas. The calculated resonant-frequency results are in very good agreement with the experimental results reported elsewhere.

Neural computation of resonant frequency of electrically thin and thick rectangular microstrip antennas

This paper presents a bacterial foraging algorithm (BFA) for null steering of linear antenna arrays by controlling only the element amplitudes. The BFA is a new evolutionary computing technique based on the foraging behavior of Escherichia (E.) coli bacteria in human intestine. To show the accuracy and flexibility of the proposed BFA, several examples of Chebyshev array pattern with the imposed single, multiple and broad nulls are given. It is found that the nulling method based on BFA is capable of steering the array nulls precisely to the undesired interference directions.

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Interference suppression of linear antenna arrays by amplitude-only control using a bacterial foraging algorithm

new method for calculating the resonant frequency of an equilateral triangular microstrip patch antenna, based on artificial neural networks, is presented. The back-propagation algorithm is used to train the networks. The theoretical resonant frequency results obtained by using this method are in very good agreement with the experimental results reported elsewhere. © 1997 John Wiley & Sons, Inc. Microwave Opt Technol Lett 14, 89–93, 1997

Kerim Guney

Papers

Amplitude-Only Pattern Nulling of Linear Antenna Arrays with the Use of Bees Algorithm

Null steering of linear antenna arrays using a modified tabu search algorithm

Neural computation of resonant frequency of electrically thin and thick rectangular microstrip antennas

Interference suppression of linear antenna arrays by amplitude-only control using a bacterial foraging algorithm

Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks