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Intelligent Computational Optimization in Engineering

Techniques & Applications

  • Book
  • © 2011

Overview

Editors:
  1. Mario Köppen

    1. Dept. Artificial Intelligence, Kyushu Institute of Technology, Izuka-Shi, Japan

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  2. Gerald Schaefer

    1. School of Engineering and Applied Science, Aston University, Birmingham, U.K.

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  3. Ajith Abraham

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

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  • Reports recent research results on intelligent computational optimization techniques in engineering
  • Written by leading experts in this field
  • State-of-the-Art book

Part of the book series: Studies in Computational Intelligence (SCI, volume 366)

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Table of contents (13 chapters)

  1. Front Matter

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  2. Intelligent Computational Optimization in Engineering: Techniques and Applications

    1. Intelligent Computational Optimization in Engineering: Techniques and Applications

      • Lars Nolle, Mario Köppen, Gerald Schaefer, Ajith Abraham
      Pages 1-24

  3. Part I: Frameworks

    1. Front Matter

      Pages 25-25
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    2. Learning Automata in Control Planning Strategies

      • Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros, Raúl Rojas
      Pages 27-54

    3. Optimization Strategies for Restricted Candidate Lists in Field Service Scheduling

      • Marko Žerdin, Alexander Gibrekhterman, Uzi Zahavi, Dovi Yellin
      Pages 55-83

    4. Framework for Integrating Optimization and Heuristic Models for Solving Planning and Scheduling Problem in a Resin Manufacturing Plant

      • Mohamed K. Omar, Yasothei Suppiah
      Pages 85-107

    5. Evolutionary Algorithms in the Optimal Sizing of Analog Circuits

      • Esteban Tlelo-Cuautle, Ivick Guerra-Gómez, Luis Gerardo de la Fraga, Georgina Flores-Becerra, Said Polanco-Martagón, Mourad Fakhfakh et al.
      Pages 109-138

  4. Part II: Algorithm Integration

    1. Front Matter

      Pages 139-139
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    2. Application of Estimation of Distribution Algorithms for Nuclear Fuel Management

      • Shan Jiang, Jonathan Carter
      Pages 141-176

    3. Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing

      • Radu-Emil Precup, Radu-Codruţ David, Stefan Preitl, Emil M. Petriu, József K. Tar
      Pages 177-207

    4. Comparison of Evolutionary and Swarm Intelligence Methods for History Matching and Uncertainty Quantification in Petroleum Reservoir Models

      • Yasin Hajizadeh, Vasily Demyanov, Linah Mohamed, Mike Christie
      Pages 209-240

    5. Optimization of Multiple Traveling Salesmen Problem by a Novel Representation Based Genetic Algorithm

      • András Király, János Abonyi
      Pages 241-269

  5. Part III: Applications

    1. Front Matter

      Pages 271-271
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    2. Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

      • Paola Pellegrini, Mauro Birattari
      Pages 273-295

    3. Analogue Circuit Optimization through a Hybrid Approach

      • Mourad Fakhfakh, Amin Sallem, Mariam Boughariou, Sameh Bennour, Eya Bradai, Emna Gaddour et al.
      Pages 297-327

    4. Evolutionary Inventory Control for Multi-Echelon Systems

      • Dilay Çelebi
      Pages 329-356

    5. Fuzzy Skyhook Surface Control Using Micro-Genetic Algorithm for Vehicle Suspension Ride Comfort

      • Yi Chen
      Pages 357-394

  6. Back Matter

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Keywords

About this book

We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches.

 

This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.

Editors and Affiliations

  • Dept. Artificial Intelligence, Kyushu Institute of Technology, Izuka-Shi, Japan

    Mario Köppen

  • School of Engineering and Applied Science, Aston University, Birmingham, U.K.

    Gerald Schaefer

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

    Ajith Abraham

Bibliographic Information

  • Book Title: Intelligent Computational Optimization in Engineering

  • Book Subtitle: Techniques & Applications

  • Editors: Mario Köppen, Gerald Schaefer, Ajith Abraham

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-21705-0

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2011

  • Hardcover ISBN: 978-3-642-21704-3Published: 15 July 2011

  • Softcover ISBN: 978-3-642-26940-0Published: 27 November 2013

  • eBook ISBN: 978-3-642-21705-0Published: 15 July 2011

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 400

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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