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Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms Cover

Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Publisher:Bentham Science
By: Andre A Keller  
Paid access
|Apr 2019
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Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems.

This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions metaheuristic algorithms genetic search algorithms and evolution strategies decomposition algorithms hybridization of different metaheuristics and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them the package NSGA-II is used to approximate the Pareto-optimal front.

It is an essential handbook for students and teachers involved in advanced optimization courses in engineering information science and mathematics degree programs.
PDF ISBN: 978-1-68108-705-4
Publisher: Bentham Science
Copyright owner: © 2019 Bentham Science Publishers
Publication date: 2019
Language: English
Pages: 300
Related subjects:
Mathematics,
General mathematics

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