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Foundations of Computational Intelligence Volume 3

Global Optimization

  • Book
  • © 2009

Overview

Editors:
  1. Ajith Abraham

    1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

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  2. Aboul-Ella Hassanien

    1. College of Business Administration Quantitative and Information System Department, Kuwait University, Safat, Kuwait

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  3. Patrick Siarry

    1. Université Paris XII Fac. Sciences, LERISS, Créteil, France

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  4. Andries Engelbrecht

    1. Department of Computer Science, University of Pretoria, Pretoria, South Africa

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  • Third volume of a Reference work on the foundations of Computational Intelligence
  • Devoted to Global Optimization

Part of the book series: Studies in Computational Intelligence (SCI, volume 203)

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Table of contents (17 chapters)

  1. Front Matter

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  2. Global Optimization Algorithms: Theoretical Foundations and Perspectives

    1. Front Matter

      Pages 1-1
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    2. Genetic Algorithms for the Use in Combinatorial Problems

      • Václav Snášel, Jan PlatoÅ¡, Pavel Krömer, Nabil Ouddane
      Pages 3-22

    3. Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications

      • Swagatam Das, Arijit Biswas, Sambarta Dasgupta, Ajith Abraham
      Pages 23-55

    4. Global Optimization Using Harmony Search: Theoretical Foundations and Applications

      • Zong Woo Geem
      Pages 57-73

    5. Hybrid GRASP Heuristics

      • Paola Festa, Mauricio G. C. Resende
      Pages 75-100

    6. Particle Swarm Optimization: Performance Tuning and Empirical Analysis

      • Millie Pant, Radha Thangaraj, Ajith Abraham
      Pages 101-128

    7. Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study

      • Djamal Habet
      Pages 129-151

    8. Reformulations in Mathematical Programming: A Computational Approach

      • Leo Liberti, Sonia Cafieri, Fabien Tarissan
      Pages 153-234

    9. Graph-Based Local Elimination Algorithms in Discrete Optimization

      • Oleg Shcherbina
      Pages 235-266

    10. Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems

      • Zikrija Avdagić, Samim Konjicija, Samir Omanović
      Pages 267-289

    11. Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning

      • Ajith Abraham, Hongbo Liu
      Pages 291-312

  3. Global Optimization Algorithms: Applications

    1. Front Matter

      Pages 313-313
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    2. An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy

      • Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu
      Pages 315-346

    3. Evolutionary Computing in Statistical Data Analysis

      • Roberto Baragona, Francesco Battaglia
      Pages 347-386

    4. Meta-heuristics for System Design Engineering

      • Rachid Chelouah, Claude Baron, Marc Zholghadri, Citlalih Gutierrez
      Pages 387-423

    5. Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms

      • Elizabeth F. Gouvêa Goldbarg, Marco C. Goldbarg
      Pages 425-460

    6. Multi-objective Team Forming Optimization for Integrated Product Development Projects

      • Hisham M. E. Abdelsalam
      Pages 461-478

    7. Genetic Algorithms for Task Scheduling Problem

      • Fatma A. Omara, Mona M. Arafa
      Pages 479-507

    8. PSO_Bounds: A New Hybridization Technique of PSO and EDAs

      • Mohammed El-Abd, Mohamed S. Kamel
      Pages 509-526
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Keywords

About this book

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

Editors and Affiliations

  • Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

    Ajith Abraham

  • College of Business Administration Quantitative and Information System Department, Kuwait University, Safat, Kuwait

    Aboul-Ella Hassanien

  • Université Paris XII Fac. Sciences, LERISS, Créteil, France

    Patrick Siarry

  • Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

Bibliographic Information

  • Book Title: Foundations of Computational Intelligence Volume 3

  • Book Subtitle: Global Optimization

  • Editors: Ajith Abraham, Aboul-Ella Hassanien, Patrick Siarry, Andries Engelbrecht

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-01085-9

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-642-01084-2Published: 27 April 2009

  • Softcover ISBN: 978-3-642-10165-6Published: 28 October 2010

  • eBook ISBN: 978-3-642-01085-9Published: 01 May 2009

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 528

  • Topics: Artificial Intelligence, Mathematical and Computational Engineering

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