Biologically-inspired optimisation methods : parallel algorithms, systems and applications
著者
書誌事項
Biologically-inspired optimisation methods : parallel algorithms, systems and applications
（Studies in computational intelligence, v. 210）
Springer, c2009
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Throughout the evolutionary history of this planet, biological systems have been able to adapt, survive and ?ourish despite the turmoils and upheavals of the environment. This ability has long fascinated and inspired people to emulate and adapt natural processes for application in the arti?cial world of human endeavours. The realm of optimisation problems is no exception. In fact, in recent years biological systems have been the inspiration of the majority of meta-heuristic search algorithms including, but not limited to, genetic algorithms,particle swarmoptimisation, ant colony optimisation and extremal optimisation. This book presentsa continuum ofbiologicallyinspired optimisation,from the theoretical to the practical. We begin with an overview of the ?eld of biologically-inspired optimisation, progress to presentation of theoretical analysesandrecentextensionstoavarietyofmeta-heuristicsand?nallyshow application to a number of real-worldproblems. As such, it is anticipated the book will provide a useful resource for reseachers and practitioners involved in any aspect of optimisation problems. The overviewof the ?eld is provided by two works co-authored by seminal thinkers in the ?
eld. Deb's "Evolution's Niche in Multi-Criterion Problem Solving", presents a very comprehensive and complete overview of almost all major issues in Evolutionary Multi-objective Optimisation (EMO). This chapter starts with the original motivation for developing EMO algorithms and provides an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts.
目次
Evolution's Niche in Multi-Criterion Problem Solving.- Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization.- Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments.- Dynamic Problems and Nature Inspired Meta-heuristics.- Relaxation Labelling Using Distributed Neural Networks.- Extremal Optimisation for Assignment Type Problems.- Niching for Ant Colony Optimisation.- Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas.- The Radio Network Design Optimization Problem.- Strategies for Decentralised Balancing Power.- An Analysis of Dynamic Mutation Operators for Conformational Sampling.- Evolving Computer Chinese Chess Using Guided Learning.
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