Non-Convex Multi-Objective Optimization. 1st ed. 2017

種類:

電子ブック

責任表示:

by Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2017

著者名:

• Pardalos, Panos M.
• Žilinskas, Antanas.
• Žilinskas, Julius.
• SpringerLink (Online service)

シリーズ名:

Springer Optimization and Its Applications ; 123

ISBN:

9783319610078 [3319610074]    

注記:

1. Definitions and Examples -- 2. Scalarization -- 3. Approximation and Complexity -- 4. A Brief Review of Non-Convex Single-Objective Optimization -- 5. Multi-Objective Branch and Bound -- 6. Worst-Case Optimal Algorithms -- 7. Statistical Models Based Algorithms -- 8. Probabilistic Bounds in Multi-Objective Optimization -- 9. Visualization of a Set of Pareto Optimal Decisions -- 10. Multi-Objective Optimization Aided Visualization of Business Process Diagrams. –References -- Index.
Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in

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