SEARCH ALGORITHMS FOR ENGINEERING OPTIMIZATION Edited by Taufik Abrão Search Algorithms for Engineering Optimization http://dx.doi.org/10.5772/45841 Edited by Taufik Abrão Contributors Abdelkader Zeblah, Rami Abdelkader, Yoshio Uwano, Bruno Augusto Angélico, Márcio Mendonça, Lúcia Valéria R. De Arruda, Taufik Abrão, Fabio Durand, Alysson Santos, , Larissa Melo, Lucas Garcia, Oleksiy Pogrebnyak, Enrique Guzmán, Juan Gabriel Zambrano Nila, Fernando Ciriaco, Paul Jean E. Jeszensky, Lucas Dias H. Sampaio, Mateus De Paula Marques, Mário Henrique Adaniya, Aleksandar Jevtić, Bo Li, Chung-Ming Kuo, Ivan Casella, Alfeu Sguarezi, Carlos Capovilla, Ernesto Ruppert, José Puma, Hamid Reza Baghaee Majidi Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2013 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Iva Lipovic Technical Editor InTech DTP team Cover InTech Design team First published February, 2013 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Search Algorithms for Engineering Optimization, Edited by Taufik Abrão p. cm. ISBN 978-953-51-0983-9 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface VII Section 1 Image Reconstruction 1 Chapter 1 Search Algorithm for Image Recognition Based on Learning Algorithm for Multivariate Data Analysis 3 Juan G. Zambrano, E. Guzmán-Ramírez and Oleksiy Pogrebnyak Chapter 2 Ant Algorithms for Adaptive Edge Detection 23 Aleksandar Jevtić and Bo Li Chapter 3 Content-Based Image Feature Description and Retrieving 45 Nai-Chung Yang, Chung-Ming Kuo and Wei-Han Chang Section 2 Telecommunication Applications 79 Chapter 4 Multidimensional Optimization-Based Heuristics Applied to Wireless Communication Systems 81 Fernando Ciriaco, Taufik Abrão and Paul Jean E. Jeszensky Chapter 5 Ant Colony Optimization for Resource Allocation and Anomaly Detection in Communication Networks 109 Lucas Hiera Dias Sampaio, Mateus de Paula Marques, Mário H. A. C. Adaniya, Taufik Abrão and Paul Jean E. Jeszensky Chapter 6 Optical Network Optimization Based on Particle Swarm Intelligence 143 Fábio Renan Durand, Larissa Melo, Lucas Ricken Garcia, Alysson José dos Santos and Taufik Abrão Section 3 Power Systems and Industrial Processes Applications 173 Chapter 7 An Adaptive Neuro-Fuzzy Strategy for a Wireless Coded Power Control in Doubly-Fed Induction Aerogenerators 175 I. R. S. Casella, A. J. Sguarezi Filho, C. E. Capovilla, J. L. Azcue and E. Ruppert Chapter 8 Application of Harmony Search Algorithm in Power Engineering 201 H. R. Baghaee, M. Mirsalim and G. B. Gharehpetian Chapter 9 Heuristic Search Applied to Fuzzy Cognitive Maps Learning 221 Bruno Augusto Angélico, Márcio Mendonça, Lúcia Valéria R. de Arruda and Taufik Abrão Chapter 10 Optimal Allocation of Reliability in Series Parallel Production System 241 Rami Abdelkader, Zeblah Abdelkader, Rahli Mustapha and Massim Yamani Section 4 Grover-Type Quantum Search 259 Chapter 11 Geometry and Dynamics of a Quantum Search Algorithm for an Ordered Tuple of Multi-Qubits 261 Yoshio Uwano ContentsVI Preface Heuristic Search is an important sub-discipline of optimization theory and finds applications in a vast variety of fields, including life science and engineering. Over the years, search meth‐ ods have made an increasing number of appearances in engineering systems, primarily be‐ cause of the capability in providing effective near-optimum solutions with low-complexity, more cost-effective and less time consuming. Heuristic Search is a method that might not al‐ ways find the best solution but is guaranteed to find a good solution in reasonable time, i.e., by sacrificing completeness it increases efficiency. Search methods have been useful in solving tough engineering-oriented problems that either could not be solved any other way or solu‐ tions take a very long time to be computed. The primary goal of this book is to provide a variety of applications for search methods and techniques in different fields of electrical engineering. By organizing relevant results and appli‐ cations, the book will serve as a useful resource for students, researchers and practitioners to further exploit the potential of search methods in solving hard non-polynomial optimization problems that arise in advanced engineering technologies, such as image and video processing issues, detection and resource allocation in telecommunication systems, security and harmonic reduction in power generation systems, as well as redundancy optimization problem and search-fuzzy learning mechanisms in industrial applications. To better explore those engineer‐ ing-oriented search methods, this book is organized in four parts. In Part 1, three search optimi‐ zation procedures applied to image and video processing are discussed. In Part 2, three specific hard optimization problems that arise in telecommunications systems are solved using guided search procedures: multiuser detection, power-rate allocation, anomaly detection and routing optical channel allocation problems are treaded deploying a collection of guided-search algo‐ rithms, such as Ant Colony, Particle Swarm, Genetic, Simulation Annealing, Tabu, Evolutionary Programming, Neighborhood Search and Hyper-Heuristic. Search methods applied to power systems and industrial processes are developed in Part 3: cognitive concepts and methods, such as fuzzy cognitive maps and adaptive fuzzy learning mechanisms are aggregated in order to efficiently model and solve optimization problems found in reliable power generation and in‐ dustrial applications. Finally, the last chapter is devoted to conceptual and formal aspects of Grover-type quantum search, which constitutes Part 4. It is our sincere hope that the book will help readers to further explore the potential of search methods in solving efficiently hard-complexity engineering optimization problems. Taufik Abrão Electrical Engineering Department, State University of Londrina (DEEL-UEL), Londrina, Paraná, Brazil Section 1 Image Reconstruction [...]... visibility is application-related, and for the TSP it is set to be inversely proportional to the node’s Euclidean distance: 1 ηij = (2) dij 26 4 Search Algorithms for Engineering Optimization Search Algorithms It can be concluded from the equations (1) and (2) that the ants favor the edges that are shorter and contain a higher concentration of pheromone AS is performed in iterations At the end of each... Finally, Section 5 contains the conclusions of this Chapter 5 6 Search Algorithms for Engineering Optimization 2 Learning Algorithm for Multivariate Data Analysis The Learning Algorithm for Multivariate Data Analysis (LAMDA) is an incremental concep‐ tual clustering method based on fuzzy logic, which can be applied in the processes of forma‐ tion and recognition of concepts (classes) LAMDA has the... distribution, and reproduction in any medium, provided the original work is properly cited 24 2 Search Algorithms for Engineering Optimization Search Algorithms digital images, pixels define the discrete space in which the artificial ants move and the edge pixels represent the food The edge detection operation is performed on a set of grayscale images The first proposed method extracts the edges from the original... unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited 4 Search Algorithms for Engineering Optimization form ridgeline thinning which improves the quality of the extracted lines for further proc‐ essing, and hence increases the overall system performance [6] In [16], Yang and Park developed a fingerprint verification system based on a set of invari‐... 3.2 Search algorithm for image recognition based on LAMDA The proposed search algorithm performs the recognition task according to a membership criterion, computed in four stages Stage 1 Image normalization: Before using the descriptors of the image in the search algo‐ rithm LAMDA, it must be normalized: xi = % % xi - xmin x = Li xmax - xmin 2 - 1 (7) ˜ wherei = 1, 2, , n, x i is the descriptor before... 0% 0-1 100% 100% Product 100% 100% Binomial 40% 100% 1 30% 100% Min-max 100% 100% Binomial 60% 0% 0% 0% 0% Table 3 Performance results (recognition rate) showed by the proposed search algorithm withaltered versions of the test images of set-1 15 16 Search Algorithms for Engineering Optimization Image Fuzzy Aggregation Exigency distribution operator level (α) original 0% Distortion percentage added... d’optimisation des Partitions PhD Thesis l’Uni‐ versité de Toulouse 21 22 Search Algorithms for Engineering Optimization [38] Orantes, A., Kempowsky, T., Lann-V, M., Prat, L., Elgue, L., Gourdon, S., Cabassud, C., & , M (2007) Selection of sensors by a new methodology coupling a classification technique and entropy criteria Chemical engineering research & design Journal, 825-38 [39] Guzmán, E., Zambrano, J G.,... membership function must be unique That is, the same element can‐ not map to different degrees of membership for the same fuzzy set 7 8 Search Algorithms for Engineering Optimization The MAD is a membership function derived from a fuzzy generalization of a binomial prob‐ ability law [26] As before, x j = ( x1, , xn ), and let E be a non-empty, proper subset of X We have an experiment where the result... biological processes [35], distribution systems of electrical energy [36], processes for drinking water produc‐ tion [29], monitoring and diagnosis of industrial processes [37], selection of sensors [38], vector quantization [39] 9 10 Search Algorithms for Engineering Optimization 3 Image recognition based on Learning Algorithm for Multivariate Data Analysis In this section the image recognition algorithm... image Multiscale adaptive gain I1 IN-1 I2 IN AS AS AS AS O1 O2 O N-1 Threshold Thinning Binary edge image Figure 1 Block diagram of the proposed edge detection method ON Search Algorithms for Engineering Optimization Search Algorithms 1 1 G(I) = I B = 0.45 k = 10 B = 0.45 k = 20 B = 0.45 k = 40 0.8 0.6 G(I) = I B = 0.2 k = 20 B = 0.45 k = 20 B = 0.7 k = 20 0.8 0.6 0.4 0.4 0.2 0.2 Output image 6 . SEARCH ALGORITHMS FOR ENGINEERING OPTIMIZATION Edited by Taufik Abrão Search Algorithms for Engineering Optimization http://dx.doi.org/10.5772/45841 Edited. achieve a better performance in terms of the robustness, generalization ability, or recognition accuracy. Search Algorithms for Engineering Optimization 4 A face recognition system for personal identification. descriptor d i for the class c l [23]. Search Algorithms for Engineering Optimization 6 2.1.1. Marginal Adequacy Degree Given an object x j and a classc l , LAMDA computes for every descriptor