Multi-Objective Optimization Evolutionary to Hybrid Framework

This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving variou...

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Bibliographic Details
Other Authors: Mandal, Jyotsna K. (Editor), Mukhopadhyay, Somnath (Editor), Dutta, Paramartha (Editor)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2018, 2018
Edition:1st ed. 2018
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary:This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems
Physical Description:XVI, 318 p. 90 illus., 51 illus. in color online resource
ISBN:9789811314711