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Full Description
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.
Contents
Section I: Introduction.- Why Is Optimization Difficult?.- The Rationale Behind Seeking Inspiration from Nature.- Section II: 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.- Section III: 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.- Section IV: Social-Natural Intelligence.- Algorithms Inspired in Social Phenomena.- Artificial Immune Systems for Optimization.- Section V: Multi-Objective Optimisation.- Ranking Methods in Many-Objective Evolutionary Algorithms.- On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II.- Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning.- Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz's Model with Application to the Caracas Stock Exchange.
