Bechikh / Datta / Gupta Recent Advances in Evolutionary Multi-objective Optimization

1. Auflage 2017
ISBN: 978-3-319-42978-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 179 Seiten

Reihe: Engineering (R0)

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

Zielgruppe

Research

Autoren/Hrsg.

Bechikh, Slim

Datta, Rituparna

Gupta, Abhishek

Inhaltsverzeichnis

Multi-objective Optimization: Classicaland Evolutionary Approaches.- Dynamic Multi-objective Optimization using EvolutionaryAlgorithms: A Survey.- Evolutionary Bilevel Optimization: An Introductionand Recent Advances.- Many-objective Optimization using Evolutionary Algorithms:A Survey.- On the Emerging Notion of EvolutionaryMultitasking: A Computational Analog of CognitiveMultitasking.- Practical Applications in Constrained EvolutionaryMulti-objective Optimization.