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Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
André A. Keller
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Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms
André A. Keller
Informazioni sul libro
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.
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Indice dei contenuti
- Cover
- Table of Contents
- Title
- BENTHAM SCIENCE PUBLISHERS LTD.
- PREFACE
- ACKNOWLEDGEMENTS
- Pareto-Optimal Front Determination
- Metaheuristic Optimization Algorithms
- Evolutionary Strategy Algorithms
- Genetic Search Algorithms
- Evolution Strategy Algorithms
- Swarm Intelligence and Co-Evolutionary Algorithms
- Decomposition-Based and Hybrid Evolutionary Algorithms
- Many-Objective Optimization and Parallel Computation
- Design of Test Problems
- Fifty Collected Test Functions
- List of Abbreviations
- List of Journal Abbreviations in the References
- List of Symbols