Advanced Optimization by Nature-Inspired Algorithms
โ Scribed by Bozorg-Haddad, Omid
- Year
- 0
- Tongue
- English
- Leaves
- 166
- Series
- Studies in Computational Intelligence 720
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Front Matter....Pages i-xv
Introduction....Pages 1-8
Cat Swarm Optimization (CSO) Algorithm....Pages 9-18
League Championship Algorithm (LCA)....Pages 19-30
Anarchic Society Optimization (ASO) Algorithm....Pages 31-38
Cuckoo Optimization Algorithm (COA)....Pages 39-49
Teaching-Learning-Based Optimization (TLBO) Algorithm....Pages 51-58
Flower Pollination Algorithm (FPA)....Pages 59-67
Krill Herd Algorithm (KHA)....Pages 69-79
Grey Wolf Optimization (GWO) Algorithm....Pages 81-91
Shark Smell Optimization (SSO) Algorithm....Pages 93-103
Ant Lion Optimizer (ALO) Algorithm....Pages 105-116
Gradient Evolution (GE) Algorithm....Pages 117-130
Moth-Flame Optimization (MFO) Algorithm....Pages 131-141
Crow Search Algorithm (CSA)....Pages 143-149
Dragonfly Algorithm (DA)....Pages 151-159
๐ SIMILAR VOLUMES
<p><i>Nature-Inspired Optimization Algorithms</i> provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with wel
Nature Inspired Optimization Algorithms is a comprehensive book on the most popular optimization algorithms that are based on nature. It starts with an overview of optimization and goes from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that
<p>Nature Inspired Optimization Algorithms is a comprehensive book on the most popular optimization algorithms that are based on nature. It starts with an overview of optimization and goes from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna t
<p><i>Nature-Inspired Optimization Algorithms</i> provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with wel