Evolutionary optimization methods for engineering, Part II, Particle swarm optimization:
Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subjec...
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| Main Author: | |
|---|---|
| Format: | Electronic Video |
| Language: | English |
| Published: |
United States
IEEE
2011
|
| Subjects: | |
| Online Access: | FHN01 TUM01 |
| Summary: | Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subject of this course which is divided into two parts. This course is Part II and covers the applications of Particle Swarm Optimization (PSO). The underlying fundamental concepts of Particle Swarm Optimization are presented and then some representative examples are provided. Useful references are listed to allow for more in-depth understanding of this topic |
| Item Description: | Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 13, 2020) |
| Physical Description: | 1 Online-Resource (1 Videodatei, 60 Minuten) color illustrations |
| ISBN: | 9781612845067 |
Staff View
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| spelling | Rahmat-Samii, Yahya Verfasser aut Evolutionary optimization methods for engineering, Part II, Particle swarm optimization Yahya Rahmat-Samii United States IEEE 2011 1 Online-Resource (1 Videodatei, 60 Minuten) color illustrations tdi rdacontent c rdamedia cr rdacarrier Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 13, 2020) Optimization is the process of upgrading something to perform better. Engineers constantly look for improving their designs in multi parametric solution space. Imagine that you will be able to use nature's evolutionary processes to obtain the best parameters for your designs. This is the subject of this course which is divided into two parts. This course is Part II and covers the applications of Particle Swarm Optimization (PSO). The underlying fundamental concepts of Particle Swarm Optimization are presented and then some representative examples are provided. Useful references are listed to allow for more in-depth understanding of this topic Genetic algorithms Evolutionary computation (DE-588)4017102-4 Film gnd-content |
| spellingShingle | Rahmat-Samii, Yahya Evolutionary optimization methods for engineering, Part II, Particle swarm optimization Genetic algorithms Evolutionary computation |
| subject_GND | (DE-588)4017102-4 |
| title | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization |
| title_auth | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization |
| title_exact_search | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization |
| title_exact_search_txtP | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization |
| title_full | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization Yahya Rahmat-Samii |
| title_fullStr | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization Yahya Rahmat-Samii |
| title_full_unstemmed | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization Yahya Rahmat-Samii |
| title_short | Evolutionary optimization methods for engineering, Part II, Particle swarm optimization |
| title_sort | evolutionary optimization methods for engineering part ii particle swarm optimization |
| topic | Genetic algorithms Evolutionary computation |
| topic_facet | Genetic algorithms Evolutionary computation Film |
| work_keys_str_mv | AT rahmatsamiiyahya evolutionaryoptimizationmethodsforengineeringpartiiparticleswarmoptimization |