Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms

1st Edition - February 17, 2014

Write a review

  • Author: Xin-She Yang
  • Paperback ISBN: 9780128100608
  • eBook ISBN: 9780124167452

Purchase options

Purchase options
Available
DRM-free (EPub, PDF, Mobi)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

Description

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.

Key Features

  • Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
  • Provides a theoretical understanding as well as practical implementation hints
  • Provides a step-by-step introduction to each algorithm

Readership

Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and engineers.

Table of Contents

  • Preface

    1: Introduction to Algorithms

    1.1 What is an Algorithm?

    1.2 Newton’s Method

    1.3 Optimization

    1.4 Search for Optimality

    1.5 No-Free-Lunch Theorems

    1.6 Nature-Inspired Metaheuristics

    1.7 A Brief History of Metaheuristics

    2: Analysis of Algorithms

    2.1 Introduction

    2.2 Analysis of Optimization Algorithms

    2.3 Nature-Inspired Algorithms

    2.4 Parameter Tuning and Parameter Control

    2.5 Discussions

    2.6 Summary

    3: Random Walks and Optimization

    3.1 Random Variables

    3.2 Isotropic Random Walks

    3.3 Lévy Distribution and Lévy Flights

    3.4 Optimization as Markov Chains

    3.5 Step Sizes and Search Efficiency

    3.6 Modality and Intermittent Search Strategy

    3.7 Importance of Randomization

    3.8 Eagle Strategy

    4: Simulated Annealing

    4.1 Annealing and Boltzmann Distribution

    4.2 Parameters

    4.3 SA Algorithm

    4.4 Unconstrained Optimization

    4.5 Basic Convergence Properties

    4.6 SA Behavior in Practice

    4.7 Stochastic Tunneling

    5: Genetic Algorithms

    5.1 Introduction

    5.2 Genetic Algorithms

    5.3 Role of Genetic Operators

    5.4 Choice of Parameters

    5.5 GA Variants

    5.6 Schema Theorem

    5.7 Convergence Analysis

    6: Differential Evolution

    6.1 Introduction

    6.2 Differential Evolution

    6.3 Variants

    6.4 Choice of Parameters

    6.5 Convergence Analysis

    6.6 Implementation

    7: Particle Swarm Optimization

    7.1 Swarm Intelligence

    7.2 PSO Algorithm

    7.3 Accelerated PSO

    7.4 Implementation

    7.5 Convergence Analysis

    7.6 Binary PSO

    8: Firefly Algorithms

    8.1 The Firefly Algorithm

    8.2 Algorithm Analysis

    8.3 Implementation

    8.4 Variants of the Firefly Algorithm

    8.5 Firefly Algorithms in Applications

    8.6 Why the Firefly Algorithm is Efficient

    9: Cuckoo Search

    9.1 Cuckoo Breeding Behavior

    9.2 Lévy Flights

    9.3 Cuckoo Search

    9.4 Why Cuckoo Search is so Efficient

    9.5 Global Convergence: Brief Mathematical Analysis

    9.6 Applications

    10: Bat Algorithms

    10.1 Echolocation of Bats

    10.2 Bat Algorithms

    10.3 Implementation

    10.4 Binary Bat Algorithms

    10.5 Variants of the Bat Algorithm

    10.6 Convergence Analysis

    10.7 Why the Bat Algorithm is Efficient

    10.8 Applications

    11: Flower Pollination Algorithms

    11.1 Introduction

    11.2 Flower Pollination Algorithm

    11.3 Multi-Objective Flower Pollination Algorithms

    11.4 Validation and Numerical Experiments

    11.5 Applications

    11.6 Further Research Topics

    12: A Framework for Self-Tuning Algorithms

    12.1 Introduction

    12.2 Algorithm Analysis and Parameter Tuning

    12.3 Framework for Self-Tuning Algorithms

    12.4 A Self-Tuning Firefly Algorithm

    12.5 Some Remarks

    13: How to Deal with Constraints

    13.1 Introduction and Overview

    13.2 Method of Lagrange Multipliers

    13.3 KKT Conditions

    13.4 Penalty Method

    13.5 Equality with Tolerance

    13.6 Feasibility Rules and Stochastic Ranking

    13.7 Multi-objective Approach to Constraints

    13.8 Spring Design

    13.9 Cuckoo Search Implementation

    14: Multi-Objective Optimization

    14.1 Multi-Objective Optimization

    14.2 Pareto Optimality

    14.3 Weighted Sum Method

    14.4 Utility Method

    14.5 The -Constraint Method

    14.6 Metaheuristic Approaches

    14.7 NSGA-II

    15: Other Algorithms and Hybrid Algorithms

    15.1 Ant Algorithms

    15.2 Bee-Inspired Algorithms

    15.3 Harmony Search

    15.4 Hybrid Algorithms

    15.5 Final Remarks

    Appendix A: Test Function Benchmarks for Global Optimization

    Appendix B: Matlab Programs

    B.1 Simulated Annealing

    B.2 Particle Swarm Optimization

    B.3 Differential Evolution

    B.4 Firefly Algorithm

    B.5 Cuckoo Search

    B.6 Bat Algorithm

    B.7 Flower Pollination Algorithm

Product details

  • No. of pages: 300
  • Language: English
  • Copyright: © Elsevier 2014
  • Published: February 17, 2014
  • Imprint: Elsevier
  • Paperback ISBN: 9780128100608
  • eBook ISBN: 9780124167452

About the Author

Xin-She Yang

Xin-She Yang
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Affiliations and Expertise

School of Science and Technology, Middlesex University, UK

Ratings and Reviews

Write a review

There are currently no reviews for "Nature-Inspired Optimization Algorithms"