Advanced Optimization by Nature-Inspired Algorithms
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Beschreibung
This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.
This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.
Collects a wide range of the most important algorithms which are useful in engineering Provides a step-by-step presentation of each algorithm with guidelines for coding algorithms Also provides a theoretical understanding as well as guidelines for practical implementation Describes all the algorithms with similar attention to detail, thus facilitating their reading and learning Relates the optimization algorithms to engineering optimization problems Facilitates rapid and effective learning Includes supplementary material: sn.pub/extras
Autor*in
Omid Bozorg-Haddad
Themen in »Advanced Optimization by Nature-Inspired Algorithms«
Pattern Search (PS) Algorithm Genetic Algorithm (GA) Simulated Annealing (SA) Tabu Search Algorithm (TSA) Ant Colony Optimization (ACO) Particle Swarm Optimization (PSO) Differential Evolution (DE) Harmony Search (HS) Shuffled Frog-Leaping Algorithm (SFLA) Honey-Bee Mating Optimization (HBMO) Invasive Weed Optimization (IWO) Central Force Optimization (CFO) Biogeography-Based Optimization (BBO) Firefly Algorithm (FA) Gravity Search Algorithm (GSA)