Bio-inspired Algorithms for Engineering

Book Description

Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control.



  • Presents real-time implementation and simulation results for all the proposed schemes
  • Offers a comparative analysis and rigorous analysis of the convergence of proposed algorithms
  • Provides a guide for implementing each application at the end of each chapter
  • Includes illustrations, tables and figures that facilitate the reader’s comprehension of the proposed schemes and applications

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Chapter One: Bio-inspired Algorithms
    1. Abstract
    2. 1.1. Introduction
    3. 1.2. Particle Swarm Optimization
    4. 1.3. Artificial Bee Colony Algorithm
    5. 1.4. Micro Artificial Bee Colony Algorithm
    6. 1.5. Differential Evolution
    7. 1.6. Bacterial Foraging Optimization Algorithm
    8. References
  9. Chapter Two: Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron
    1. Abstract
    2. 2.1. Introduction
    3. 2.2. Support Vector Machines
    4. 2.3. Evolutionary algorithms
    5. 2.4. The Kernel Adatron algorithm
    6. 2.5. Kernel Adatron trained with evolutionary algorithms
    7. 2.6. Results using benchmark repository datasets
    8. 2.7. Application to classify electromyographic signals
    9. 2.8. Conclusions
    10. References
  10. Chapter Three: Reconstruction of 3D Surfaces Using RBF Adjusted with PSO
    1. Abstract
    2. 3.1. Introduction
    3. 3.2. Radial basis functions
    4. 3.3. Interpolation of surfaces with RBF and PSO
    5. 3.4. Conclusion
    6. References
  11. Chapter Four: Soft Computing Applications in Robot Vision
    1. Abstract
    2. 4.1. Introduction
    3. 4.2. Image tracking
    4. 4.3. Plane detection
    5. 4.4. Conclusion
    6. References
  12. Chapter Five: Soft Computing Applications in Mobile Robotics
    1. Abstract
    2. 5.1. Introduction to mobile robotics
    3. 5.2. Nonholonomic mobile robot navigation
    4. 5.3. Holonomic mobile robot navigation
    5. 5.4. Conclusion
    6. References
  13. Chapter Six: Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems
    1. Abstract
    2. 6.1. Introduction
    3. 6.2. Particle-swarm-based approach of a real-time discrete neural identifier for Linear Induction Motors
    4. 6.3. Neural model with particle swarm optimization Kalman learning for forecasting in smart grids
    5. 6.4. Conclusions
    6. References
  14. Chapter Seven: Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System
    1. Abstract
    2. 7.1. Neural Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
    3. 7.2. Neural-PSO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
    4. 7.3. Neural-BFO Second-Order Sliding Mode Controller for unknown discrete-time nonlinear systems
    5. 7.4. Comparative analysis
    6. 7.5. Conclusions
    7. References
  15. Chapter Eight: Final Remarks
  16. Index

Product Information

  • Title: Bio-inspired Algorithms for Engineering
  • Author(s): Alma Y. Alanis, Carlos Lopez-Franco, Nancy Arana-Daniel
  • Release date: February 2018
  • Publisher(s): Butterworth-Heinemann
  • ISBN: 9780128137895