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Engineering optimization : an introduction with metaheuristic applications /
An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley,
©2010.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Yang, Xin-She. | |
| 245 | 1 | 0 | |a Engineering optimization : |b an introduction with metaheuristic applications / |c Xin-She Yang. |
| 260 | |a Hoboken, N.J. : |b John Wiley, |c ©2010. | ||
| 300 | |a 1 online resource (xxvii, 347 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 505 | 0 | |6 880-01 |a Front Matter -- Foundations of Optimization and Algorithms. A Brief History of Optimization -- Engineering Optimization -- Mathematical Foundations -- Classic Optimization Methods I -- Classic Optimization Methods II -- Convex Optimization -- Calculus of Variations -- Random Number Generators -- Monte Carlo Methods -- Random Walk and Markov Chain -- Metaheuristic Algorithms. Genetic Algorithms -- Simulated Annealing -- Ant Algorithms -- Bee Algorithms -- Particle Swarm Optimization -- Harmony Search -- Firefly Algorithm -- Applications. Multiobjective Optimization -- Engineering Applications -- Appendix A: Test Problems in Optimization -- Appendix B: MATLAB Programs -- Appendix C: Glossary -- Appendix D: Problem Solutions -- References -- Index. | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insigh. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Heuristic programming. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 6 | |a Programmation heuristique. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Mathématiques de l'ingénieur. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Reference. |2 bisacsh | |
| 650 | 7 | |a Engineering mathematics |2 fast | |
| 650 | 7 | |a Heuristic programming |2 fast | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 758 | |i has work: |a Engineering Optimization (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH64Yq4CJHQ7rDv9Fh8qHC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Yang, Xin-She. |t Engineering optimization. |d Hoboken, N.J. : John Wiley, ©2010 |z 9780470582466 |w (DLC) 2010003429 |w (OCoLC)500823432 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=565130 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g pt. I |t FOUNDATIONS OF OPTIMIZATION AND ALGORITHMS -- |g 1. |t Brief History of Optimization -- |g 1.1. |t Before 1900 -- |g 1.2. |t Twentieth Century -- |g 1.3. |t Heuristics and Metaheuristics -- |t Exercises -- |g 2. |t Engineering Optimization -- |g 2.1. |t Optimization -- |g 2.2. |t Type of Optimization -- |g 2.3. |t Optimization Algorithms -- |g 2.4. |t Metaheuristics -- |g 2.5. |t Order Notation -- |g 2.6. |t Algorithm Complexity -- |g 2.7. |t No Free Lunch Theorems -- |t Exercises -- |g 3. |t Mathematical Foundations -- |g 3.1. |t Upper and Lower Bounds -- |g 3.2. |t Basic Calculus -- |g 3.3. |t Optimality -- |g 3.3.1. |t Continuity and Smoothness -- |g 3.3.2. |t Stationary Points -- |g 3.3.3. |t Optimality Criteria -- |g 3.4. |t Vector and Matrix Norms -- |g 3.5. |t Eigenvalues and Definiteness -- |g 3.5.1. |t Eigenvalues -- |g 3.5.2. |t Defmiteness -- |g 3.6. |t Linear and Affine Functions -- |g 3.6.1. |t Linear Functions -- |g 3.6.2. |t Affine Functions -- |g 3.6.3. |t Quadratic Form -- |g 3.7. |t Gradient and Hessian Matrices -- |g 3.7.1. |t Gradient -- |g 3.7.2. |t Hessian -- |g 3.7.3. |t Function approximations -- |g 3.7.4. |t Optimality of multivariate functions -- |g 3.8. |t Convexity -- |g 3.8.1. |t Convex Set -- |g 3.8.2. |t Convex Functions -- |t Exercises -- |g 4. |t Classic Optimization Methods I -- |g 4.1. |t Unconstrained Optimization -- |g 4.2. |t Gradient-Based Methods -- |g 4.2.1. |t Newton's Method -- |g 4.2.2. |t Steepest Descent Method -- |g 4.2.3. |t Line Search -- |g 4.2.4. |t Conjugate Gradient Method -- |g 4.3. |t Constrained Optimization -- |g 4.4. |t Linear Programming -- |g 4.5. |t Simplex Method -- |g 4.5.1. |t Basic Procedure -- |g 4.5.2. |t Augmented Form -- |g 4.6. |t Nonlinear Optimization -- |g 4.7. |t Penalty Method -- |g 4.8. |t Lagrange Multipliers -- |g 4.9. |t Karush-Kuhn-Tucker Conditions -- |t Exercises -- |g 5. |t Classic Optimization Methods II -- |g 5.1. |t BFGS Method -- |g 5.2. |t Nelder-Mead Method -- |g 5.2.1. |t Simplex -- |g 5.2.2. |t Nelder-Mead Downhill Simplex -- |g 5.3. |t Trust-Region Method -- |g 5.4. |t Sequential Quadratic Programming -- |g 5.4.1. |t Quadratic Programming -- |g 5.4.2. |t Sequential Quadratic Programming -- |t Exercises -- |g 6. |t Convex Optimization -- |g 6.1. |t KKT Conditions -- |g 6.2. |t Convex Optimization Examples -- |g 6.3. |t Equality Constrained Optimization -- |g 6.4. |t Barrier Functions -- |g 6.5. |t Interior-Point Methods -- |g 6.6. |t Stochastic and Robust Optimization -- |t Exercises -- |g 7. |t Calculus of Variations -- |g 7.1. |t Euler-Lagrange Equation -- |g 7.1.1. |t Curvature -- |g 7.1.2. |t Euler-Lagrange Equation -- |g 7.2. |t Variations with Constraints -- |g 7.3. |t Variations for Multiple Variables -- |g 7.4. |t Optimal Control -- |g 7.4.1. |t Control Problem -- |g 7.4.2. |t Pontryagin's Principle -- |g 7.4.3. |t Multiple Controls -- |g 7.4.4. |t Stochastic Optimal Control -- |t Exercises -- |g 8. |t Random Number Generators -- |g 8.1. |t Linear Congruential Algorithms -- |g 8.2. |t Uniform Distribution -- |g 8.3. |t Other Distributions -- |g 8.4. |t Metropolis Algorithms -- |t Exercises -- |g 9. |t Monte Carlo Methods -- |g 9.1. |t Estimating π -- |g 9.2. |t Monte Carlo Integration -- |g 9.3. |t Importance of Sampling -- |t Exercises -- |g 10. |t Random Walk and Markov Chain -- |g 10.1. |t Random Process -- |g 10.2. |t Random Walk -- |g 10.2.1. |t 1D Random Walk -- |g 10.2.2. |t Random Walk in Higher Dimensions -- |g 10.3. |t Levy Flights -- |g 10.4. |t Markov Chain -- |g 10.5. |t Markov Chain Monte Carlo -- |g 10.5.1. |t Metropolis-Hastings Algorithms -- |g 10.5.2. |t Random Walk -- |g 10.6. |t Markov Chain and Optimisation -- |t Exercises -- |g pt. II |t METAHEURISTIC ALGORITHMS -- |g 11. |t Genetic Algorithms -- |g 11.1. |t Introduction -- |g 11.2. |t Genetic Algorithms -- |g 11.2.1. |t Basic Procedure -- |g 11.2.2. |t Choice of Parameters -- |g 11.3. |t Implementation -- |t Exercises -- |g 12. |t Simulated Annealing -- |g 12.1. |t Annealing and Probability -- |g 12.2. |t Choice of Parameters -- |g 12.3. |t SA Algorithm -- |g 12.4. |t Implementation -- |t Exercises -- |g 13. |t Ant Algorithms -- |g 13.1. |t Behaviour of Ants -- |g 13.2. |t Ant Colony Optimization -- |g 13.3. |t Double Bridge Problem -- |g 13.4. |t Virtual Ant Algorithm -- |t Exercises -- |g 14. |t Bee Algorithms -- |g 14.1. |t Behavior of Honey Bees -- |g 14.2. |t Bee Algorithms -- |g 14.2.1. |t Honey Bee Algorithm -- |g 14.2.2. |t Virtual Bee Algorithm -- |g 14.2.3. |t Artificial Bee Colony Optimization -- |g 14.3. |t Applications -- |t Exercises -- |g 15. |t Particle Swarm Optimization -- |g 15.1. |t Swarm Intelligence -- |g 15.2. |t PSO algorithms -- |g 15.3. |t Accelerated PSO -- |g 15.4. |t Implementation -- |g 15.4.1. |t Multimodal Functions -- |g 15.4.2. |t Validation -- |g 15.5. |t Constraints -- |t Exercises -- |g 16. |t Harmony Search -- |g 16.1. |t Music-Based Algorithms -- |g 16.2. |t Harmony Search -- |g 16.3. |t Implementation -- |t Exercises -- |g 17. |t Firefly Algorithm -- |g 17.1. |t Behaviour of Fireflies -- |g 17.2. |t Firefly-Inspired Algorithm -- |g 17.2.1. |t Firefly Algorithm -- |g 17.2.2. |t Light Intensity and Attractiveness -- |g 17.2.3. |t Scaling and Global Optima -- |g 17.2.4. |t Two Special Cases -- |g 17.3. |t Implementation -- |g 17.3.1. |t Multiple Global Optima -- |g 17.3.2. |t Multimodal Functions -- |g 17.3.3. |t FA Variants -- |t Exercises -- |g pt. III |t APPLICATIONS -- |g 18. |t Multiobjective Optimization -- |g 18.1. |t Pareto Optimality -- |g 18.2. |t Weighted Sum Method -- |g 18.3. |t Utility Method -- |g 18.4. |t Metaheuristic Search -- |g 18.5. |t Other Algorithms -- |t Exercises -- |g 19. |t Engineering Applications -- |g 19.1. |t Spring Design -- |g 19.2. |t Pressure Vessel -- |g 19.3. |t Shape Optimization -- |g 19.4. |t Optimization of Eigenvalues and Frequencies -- |g 19.5. |t Inverse Finite Element Analysis -- |t Exercises -- |t Appendices -- |g Appendix |t A Test Problems in Optimization -- |g Appendix B |t Matlab® Programs -- |g B.1. |t Genetic Algorithms -- |g B.2. |t Simulated Annealing -- |g B.3. |t Particle Swarm Optimization -- |g B.4. |t Harmony Search -- |g B.5. |t Firefly Algorithm -- |g B.6. |t Large Sparse Linear Systems -- |g B.7. |t Nonlinear Optimization -- |g B.7.1. |t Spring Design -- |g B.7.2. |t Pressure Vessel -- |g Appendix C |t Glossary -- |g Appendix D |t Problem Solutions. |
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