Engineering Optimization: An Introduction with Metaheuristic Applications
Xin‐She Yang
Abstract
Open-access reader
Xin‐She Yang
Abstract
Open-access reader
An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences. \n 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 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. \n(from publisher's website)
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An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences. \n 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 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. \n(from publisher's website)
Key concepts: Metaheuristic, Parallel metaheuristic, Computer science, Engineering optimization, Search-based software engineering, Multi-swarm optimization, Simulated annealing, Harmony search