{"product_id":"engineering-optimization-isbn-9780470582466","title":"Engineering Optimization","description":"An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences  \u003cp\u003eFrom 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, \u003ci\u003eEngineering Optimization: An Introduction with Metaheuristic Applications\u003c\/i\u003e outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.\u003c\/p\u003e \u003cp\u003eThe author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eFoundations of Optimization and Algorithms\u003c\/i\u003e\u003c\/b\u003e provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eMetaheuristic Algorithms\u003c\/i\u003e\u003c\/b\u003e presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eApplications\u003c\/i\u003e\u003c\/b\u003e outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEngineering Optimization: An Introduction with Metaheuristic Applications\u003c\/i\u003e is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.\u003c\/p\u003e List of Figures.  \u003cp\u003e\u003c\/p\u003e Preface.  \u003cp\u003e\u003c\/p\u003e Acknowledgments.  \u003cp\u003e\u003c\/p\u003e Introduction.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003ePART I Foundations of Optimization and Algorithms.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e \u003cba\u003e\u003c\/ba\u003e1.1 Before 1900.  \u003cp\u003e\u003c\/p\u003e 1.2 Twentieth Century.  \u003cp\u003e\u003c\/p\u003e 1.3 Heuristics and Metaheuristics.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e2 Engineering Optimization.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 2.1 Optimization.  \u003cp\u003e\u003c\/p\u003e 2.2 Type of Optimization.  \u003cp\u003e\u003c\/p\u003e 2.3 Optimization Algorithms.  \u003cp\u003e\u003c\/p\u003e 2.4 Metaheuristics.  \u003cp\u003e\u003c\/p\u003e 2.5 Order Notation.  \u003cp\u003e\u003c\/p\u003e 2.6 Algorithm Complexity.  \u003cp\u003e\u003c\/p\u003e 2.7 No Free Lunch Theorems.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e3 Mathematical Foundations.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 3.1 Upper and Lower Bounds.  \u003cp\u003e\u003c\/p\u003e 3.2 Basic Calculus.  \u003cp\u003e\u003c\/p\u003e 3.3 Optimality.  \u003cp\u003e\u003c\/p\u003e 3.4 Vector and Matrix Norms.  \u003cp\u003e\u003c\/p\u003e 3.5 Eigenvalues and Definiteness.  \u003cp\u003e\u003c\/p\u003e 3.6 Linear and Affine Functions.  \u003cp\u003e\u003c\/p\u003e 3.7 Gradient and Hessian Matrices.  \u003cp\u003e\u003c\/p\u003e 3.8 Convexity.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e4 Classic Optimization Methods I.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 4.1 Unconstrained Optimization.  \u003cp\u003e\u003c\/p\u003e 4.2 Gradient-Based Methods.  \u003cp\u003e\u003c\/p\u003e 4.3 Constrained Optimization.  \u003cp\u003e\u003c\/p\u003e 4.4 Linear Programming.  \u003cp\u003e\u003c\/p\u003e 4.5 Simplex Method.  \u003cp\u003e\u003c\/p\u003e 4.6 Nonlinear Optimization.  \u003cp\u003e\u003c\/p\u003e 4.7 Penalty Method.  \u003cp\u003e\u003c\/p\u003e 4.8 Lagrange Multipliers.  \u003cp\u003e\u003c\/p\u003e 4.9 Karush-Kuhn-Tucker Conditions.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e5 Classic Optimization Methods II.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 5.1 BFGS Method.  \u003cp\u003e\u003c\/p\u003e 5.2 Nelder-Mead Method.  \u003cp\u003e\u003c\/p\u003e 5.3 Trust-Region Method.  \u003cp\u003e\u003c\/p\u003e 5.4 Sequential Quadratic Programming.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e6 Convex Optimization.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 6.1 KKT Conditions.  \u003cp\u003e\u003c\/p\u003e 6.2 Convex Optimization Examples.  \u003cp\u003e\u003c\/p\u003e 6.3 Equality Constrained Optimization.  \u003cp\u003e\u003c\/p\u003e 6.4 Barrier Functions.  \u003cp\u003e\u003c\/p\u003e 6.5 Interior-Point Methods.  \u003cp\u003e\u003c\/p\u003e 6.6 Stochastic and Robust Optimization.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e7 Calculus of Variations.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 7.1 Euler-Lagrange Equation.  \u003cp\u003e\u003c\/p\u003e 7.2 Variations with Constraints.  \u003cp\u003e\u003c\/p\u003e 7.3 Variations for Multiple Variables.  \u003cp\u003e\u003c\/p\u003e 7.4 Optimal Control.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e8 Random Number Generators.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 8.1 Linear Congruential Algorithms.  \u003cp\u003e\u003c\/p\u003e 8.2 Uniform Distribution.  \u003cp\u003e\u003c\/p\u003e 8.3 Other Distributions.  \u003cp\u003e\u003c\/p\u003e 8.4 Metropolis Algorithms.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e9 Monte Carlo Methods.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 9.1 Estimating p.  \u003cp\u003e\u003c\/p\u003e 9.2 Monte Carlo Integration.  \u003cp\u003e\u003c\/p\u003e 9.3 Importance of Sampling.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e10 Random Walk and Markov Chain.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 10.1 Random Process.  \u003cp\u003e\u003c\/p\u003e 10.2 Random Walk.  \u003cp\u003e\u003c\/p\u003e 10.3 Lévy Flights.  \u003cp\u003e\u003c\/p\u003e 10.4 Markov Chain.  \u003cp\u003e\u003c\/p\u003e 10.5 Markov Chain Monte Carlo.  \u003cp\u003e\u003c\/p\u003e 10.6 Markov Chain and Optimisation.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003ePART II Metaheuristic Algorithms.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e11 Genetic Algorithms.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 11.1 Introduction.  \u003cp\u003e\u003c\/p\u003e 11.2 Genetic Algorithms.  \u003cp\u003e\u003c\/p\u003e 11.3 Implementation.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e12 Simulated Annealing.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 12.1 Annealing and Probability.  \u003cp\u003e\u003c\/p\u003e 12.2 Choice of Parameters.  \u003cp\u003e\u003c\/p\u003e 12.3 SA Algorithm.  \u003cp\u003e\u003c\/p\u003e 12.4 Implementation.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e13 Ant Algorithms.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 13.1 Behaviour of Ants.  \u003cp\u003e\u003c\/p\u003e 13.2 Ant Colony Optimization.  \u003cp\u003e\u003c\/p\u003e 13.3 Double Bridge Problem.  \u003cp\u003e\u003c\/p\u003e 13.4 Virtual Ant Algorithm.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e14 Bee Algorithms.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 14.1 Behavior of Honey Bees.  \u003cp\u003e\u003c\/p\u003e 14.2 Bee Algorithms.  \u003cp\u003e\u003c\/p\u003e 14.3 Applications.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e15 Particle Swarm Optimization.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 15.1 Swarm Intelligence.  \u003cp\u003e\u003c\/p\u003e 15.2 PSO algorithms.  \u003cp\u003e\u003c\/p\u003e 15.3 Accelerated PSO.  \u003cp\u003e\u003c\/p\u003e 15.4 Implementation.  \u003cp\u003e\u003c\/p\u003e 15.5 Constraints.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e16 Harmony Search.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 16.1 Music-Based Algorithms.  \u003cp\u003e\u003c\/p\u003e 16.2 Harmony Search.  \u003cp\u003e\u003c\/p\u003e 16.3 Implementation.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e17 Firefly Algorithm.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 17.1 Behaviour of Fireflies.  \u003cp\u003e\u003c\/p\u003e 17.2 Firefly-Inspired Algorithm.  \u003cp\u003e\u003c\/p\u003e 17.3 Implementation.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003ePART III Applications.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e18 Multiobjective Optimization.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 18.1 Pareto Optimality.  \u003cp\u003e\u003c\/p\u003e 18.2 Weighted Sum Method.  \u003cp\u003e\u003c\/p\u003e 18.3 Utility Method.  \u003cp\u003e\u003c\/p\u003e 18.4 Metaheuristic Search.  \u003cp\u003e\u003c\/p\u003e 18.5 Other Algorithms.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003e19 Engineering Applications.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e 19.1 Spring Design.  \u003cp\u003e\u003c\/p\u003e 19.2 Pressure Vessel.  \u003cp\u003e\u003c\/p\u003e 19.3 Shape Optimization.  \u003cp\u003e\u003c\/p\u003e 19.4 Optimization of Eigenvalues and Frequencies.  \u003cp\u003e\u003c\/p\u003e 19.5 Inverse Finite Element Analysis.  \u003cp\u003e\u003c\/p\u003e Exercises.  \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAppendices.\u003c\/b\u003e  \u003cp\u003e\u003c\/p\u003e Appendix A: Test Problems in Optimization.  \u003cp\u003e\u003c\/p\u003e Appendix B: Matlab® Programs.  \u003cp\u003e\u003c\/p\u003e B.1 Genetic Algorithms.  \u003cp\u003e\u003c\/p\u003e B.2 Simulated Annealing.  \u003cp\u003e\u003c\/p\u003e B.3 Particle Swarm Optimization.  \u003cp\u003e\u003c\/p\u003e B.4 Harmony Search.  \u003cp\u003e\u003c\/p\u003e B.5 Firefly Algorithm.  \u003cp\u003e\u003c\/p\u003e B.6 Large Sparse Linear Systems.  \u003cp\u003e\u003c\/p\u003e B.7 Nonlinear Optimization.  \u003cp\u003e\u003c\/p\u003e B.7.1 Spring Design.  \u003cp\u003e\u003c\/p\u003e B.7.2 Pressure Vessel.  \u003cp\u003e\u003c\/p\u003e Appendix C: Glossary.  \u003cp\u003e\u003c\/p\u003e Appendix D: Problem Solutions.  \u003cp\u003e\u003c\/p\u003e References.  \u003cp\u003e\u003c\/p\u003e Index. \u003cb\u003eXIN-SHE YANG, PhD,\u003c\/b\u003e is Senior Research Fellow in the Department of Engineering at Cambridge University (United Kingdom). The Editor-in-Chief of \u003ci\u003eInternational Journal of Mathematical Modeling and Numerical Optimization (IJMMNO),\u003c\/i\u003e Dr. Yang has published more than sixty journal articles in his areas of research interest, which include computational mathematics, metaheuristic algorithms, numerical analysis, and engineering optimization.  An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences  \u003cp\u003eFrom 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, \u003ci\u003eEngineering Optimization: An Introduction with Metaheuristic Applications\u003c\/i\u003e outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.\u003c\/p\u003e \u003cp\u003eThe author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eFoundations of Optimization and Algorithms\u003c\/i\u003e\u003c\/b\u003e provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eMetaheuristic Algorithms\u003c\/i\u003e\u003c\/b\u003e presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003e\u003ci\u003eApplications\u003c\/i\u003e\u003c\/b\u003e outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEngineering Optimization: An Introduction with Metaheuristic Applications\u003c\/i\u003e is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989137670373,"sku":"NP9780470582466","price":166.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470582466.jpg?v=1761782948","url":"https:\/\/k12savings.com\/es\/products\/engineering-optimization-isbn-9780470582466","provider":"K12savings","version":"1.0","type":"link"}