Non-Convex Multi-Objective Optimization, Softcover reprint of the original 1st ed. 2017 Springer Optimization and Its Applications Series, Vol. 123
Auteurs : Pardalos Panos M., Žilinskas Antanas, Žilinskas Julius
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.
Summarizes non-convex multi-objective optimization problems and methods
Supplies comprehensive coverage, theoretical background, and examples of practical applications
Explains several directions of multi-objective optimization research
Includes supplementary material: sn.pub/extras
Date de parution : 06-2018
Ouvrage de 192 p.
15.5x23.5 cm
Date de parution : 08-2017
Ouvrage de 192 p.
15.5x23.5 cm
Thèmes de Non-Convex Multi-Objective Optimization :
Mots-clés :
Branch-and-Bound approach; Lipschitz optimization; applications in engineering; non-convex multi-objective optimization; randomized algorithms; software and applications; Scalarization; Tchebycheff Method; Pareto Sets; Normal Boundary Intersection; Statistical Models for Global Optimization; Optimal Algorithms for Lipschitz Functions; Optimal Passive Algorithm; Optimal Sequential Algorithm; Multidimensional Bi-Objective Lipschitz Optimization; Pareto Frontier; Trisection of a Hyper-rectangle; Pareto Optimal Decisions; Binary-Linear Model; continuous problems