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.

Includes multi/many-objective optimization using various computation platforms Explores the efficacy of hybridization approaches like GA-Fuzz, Neuro-Fuzz, Neuro-ACO etc. in solving real-life application problems, especially in multi-objective frameworks Offers valuable insights into the evolution of computation paradigms in the field of multi/many-objective optimization over the last few years: Why and how did it happen?

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