Nature-inspired Algorithms for Optimisation

by Chiong, Raymond

• ISBN13: 9783642002663
• ISBN10: 3642002668
• eBook ISBN(s): 9783642002670
• Format: Hardcover
• Copyright: 2009-05-01
• Publisher: Springer Verlag
• More Book Details
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Summary

Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.

Table of Contents

Introduction

Why Is Optimization Difficult?

The Rationale behind Seeking Inspiration from Nature

Evolutionary Intelligence

The Evolutionary-Gradient-Search Procedure in Theory and Practice

The Evolutionary Transition Algorithm

Evolving Complex Solutions out of Simpler Ones

A Model-Assisted Memetic Algorithm for Expensive Optimization Problems

A Self-Adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization

Differential Evolution with Fitness Diversity Self-Adaptation

Central Pattern Generators

Optimisation and Application

Collective Intelligence

Fish School Search

Magnifier Particle Swarm Optimization

Improved Particle Swarm Optimization in Constrained Numerical Search Spaces

Applying River Formation Dynamics to Solve NP-Complete Problems

Social-Natural Intelligence

Algorithms Inspired in Social Phenomena

Artif

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