Recent Advances in Evolutionary Multi-objective Optimization (eBook, PDF)

Redaktion: Bechikh, Slim; Gupta, Abhishek; Datta, Rituparna

• Format: PDF

Produktbeschreibung

This book covers the most recent advances in the field of evolutionary multiobjective

optimization. With the aim of drawing the attention of up-andcoming

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 179
• Erscheinungstermin: 9. August 2016
• Englisch
• ISBN-13: 9783319429786
• Artikelnr.: 46972630

Inhaltsangabe

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.