An accessible introduction to metaheuristics and optimization,featuring powerful and modern algorithms for application acrossengineering and the sciences
From engineering and computer science to economics andmanagement science, optimization is a core component for problemsolving. Highlighting the latest developments that have evolved inrecent years, Engineering Optimization: An Introduction withMetaheuristic Applications outlines popular metaheuristicalgorithms and equips readers with the skills needed to apply thesetechniques to their own optimization problems. With insightfulexamples from various fields of study, the author highlights keyconcepts and techniques for the successful application ofcommonly-used metaheuristc algorithms, including simulatedannealing, particle swarm optimization, harmony search, and geneticalgorithms.
The author introduces all major metaheuristic algorithms andtheir applications in optimization through a presentation that isorganized into three succinct parts:
* Foundations of Optimization and Algorithmsprovides a brief introduction to the underlying nature ofoptimization and the common approaches to optimization problems,random number generation, the Monte Carlo method, and the Markovchain Monte Carlo method
* Metaheuristic Algorithms presents commonmetaheuristic algorithms in detail, including genetic algorithms,simulated annealing, ant algorithms, bee algorithms, particle swarmoptimization, firefly algorithms, and harmony search
* Applications outlines a wide range ofapplications that use metaheuristic algorithms to solve challengingoptimization problems with detailed implementation while alsointroducing various modifications used for multi-objectiveoptimization
Throughout the book, the author presents worked-out examples andreal-world applications that illustrate the modern relevance of thetopic. A detailed appendix features important and popularalgorithms using MATLAB® and Octave software packages, and arelated FTP site houses MATLAB code and programs for easyimplementation of the discussed techniques. In addition, referencesto the current literature enable readers to investigate individualalgorithms and methods in greater detail.
Engineering Optimization: An Introduction with MetaheuristicApplications is an excellent book for courses on optimizationand computer simulation at the upper-undergraduate and graduatelevels. It is also a valuable reference for researchers andpractitioners working in the fields of mathematics, engineering,computer science, operations research, and management science whouse metaheuristic algorithms to solve problems in their everydaywork.