Del i serien Natural Computing Series

Introduction to Metaheuristics for Optimization

Inbunden, Engelska, 2018

AvBastien Chopard,Marco Tomassini

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The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies. This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Produktinformation

Utgivningsdatum2018-11-13
Mått155 x 235 x 18 mm
Vikt480 g
FormatInbunden
SpråkEngelska
SerieNatural Computing Series
Antal sidor226
FörlagSpringer International Publishing AG
ISBN9783319930725

Tillhör följande kategorier

Företagsekonomi inom Ekonomi och Ledarskap
Artificiell intelligens inom Data och IT
Ledarskap och motivation inom Ekonomi och Ledarskap
Optimering inom Naturvetenskap och teknik

Bastien Chopard is a professor in the Département d'informatique of the Université de Genève, where he directs the Scientific and Parallel Computing Group. His main research activity concerns the study of complex systems, in particular the development of new numerical methods to model and simulate phenomena in natural sciences, economics, social systems, and biomedical applications, the key tools used being cellular automata, the lattice Boltzmann method, and multiagent techniques. Marco Tomassini is an honorary professor at the Faculty of Business and Economics of the University of Lausanne (HEC). After obtaining a PhD degree in Theoretical Chemistry working on computer simulations of condensed matter systems, he switched to Computer Science and complex systems investigations. His main research activities have since focused on parallel computing, cellular automata, evolutionary algorithms, the structure of difficult problem landscapes, complex networks, and evolutionary games.

Problems, Algorithms, Computational Complexity.- Search Space.- Tabu Search.- Simulated Annealing.- Ant Colony Optimization (ACO).- Non-PSO Optimization.- Firefly Algorithm, Cuckoo Algorithm, Lévy Flights.- Evolutionary Algorithms: Foundations.- Evolutionary Algorithms: Advanced.- Phase Transition in Optimization Problems.- Performance and Limitations of Metaheuristics.- Statistical Analysis of Research Spaces.