Nature-Inspired Algorithms for Optimisation

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Raymond Chiong
Springer Science & Business Media, Apr 28, 2009 - Mathematics - 516 pages
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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.

 

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Contents

Why Is Optimization Difficult?
1
The Rationale Behind Seeking Inspiration from Nature
51
The EvolutionaryGradientSearch Procedure in Theory and Practice
77
Evolving Complex Solutions Out of Simpler Ones
103
A ModelAssisted Memetic Algorithm for Expensive Optimization Problems
132
A Selfadaptive Mixed Distribution Based Univariate Estimation of Distribution Algorithm for Large Scale Global Optimization
171
Differential Evolution with Fitness Diversity Selfadaptation
199
Optimisation and Application
235
Applying River Formation Dynamics to Solve NPComplete Problems
333
Algorithms Inspired in Social Phenomena
369
Artificial Immune Systems for Optimization
389
Ranking Methods in ManyObjective Evolutionary Algorithms
412
On the Effect of Applying a SteadyState Selection Scheme in the MultiObjective Genetic Algorithm NSGAII
435
Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning
457
Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitzs Model with Application to the Caracas Stock Exchange
489
Index
510

Fish School Search
261
Magnifier Particle Swarm Optimization
278
Improved Particle Swarm Optimization in Constrained Numerical Search Spaces
299
Author Index
515
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