Nature-Inspired Algorithms for Optimisation

✍ Scribed by Thomas Weise, Michael Zapf, Raymond Chiong, Antonio J. Nebro (auth.), Raymond Chiong (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2009
• Tongue: English
• Leaves: 523
• Series: Studies in Computational Intelligence 193
• Edition: 1
• Category: Library

✦ Synopsis

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

Front Matter....Pages -
Why Is Optimization Difficult?....Pages 1-50
The Rationale Behind Seeking Inspiration from Nature....Pages 51-76
The Evolutionary-Gradient-Search Procedure in Theory and Practice....Pages 77-101
The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones....Pages 103-131
A Model-Assisted Memetic Algorithm for Expensive Optimization Problems....Pages 133-169
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization....Pages 171-198
Differential Evolution with Fitness Diversity Self-adaptation....Pages 199-234
Central Pattern Generators: Optimisation and Application....Pages 235-260
Fish School Search....Pages 261-277
Magnifier Particle Swarm Optimization....Pages 279-298
Improved Particle Swarm Optimization in Constrained Numerical Search Spaces....Pages 299-332
Applying River Formation Dynamics to Solve NP-Complete Problems....Pages 333-368
Algorithms Inspired in Social Phenomena....Pages 369-387
Artificial Immune Systems for Optimization....Pages 389-411
Ranking Methods in Many-Objective Evolutionary Algorithms....Pages 413-434
On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II....Pages 435-456
Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning....Pages 457-487
Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange....Pages 489-509
Back Matter....Pages -

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Operations Research/Decision Theory