𝔖 Bobbio Scriptorium
✦   LIBER   ✦

[Studies in Computational Intelligence] Evolutionary Computation for Dynamic Optimization Problems Volume 490 || A Comparative Study on Particle Swarm Optimization in Dynamic Environments

✍ Scribed by Yang, Shengxiang; Yao, Xin


Book ID
120446036
Publisher
Springer Berlin Heidelberg
Year
2013
Tongue
English
Weight
506 KB
Edition
2
Category
Article
ISBN
3642384161

No coin nor oath required. For personal study only.

✦ Synopsis


This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.


πŸ“œ SIMILAR VOLUMES


[Studies in Computational Intelligence]
✍ Yang, Shengxiang; Ong, Yew-Soon; Jin, Yaochu πŸ“‚ Article πŸ“… 2007 πŸ› Springer Berlin Heidelberg 🌐 German βš– 980 KB

This Book Compiles Recent Advances Of Evolutionary Algorithms In Dynamic And Uncertain Environments Within A Unified Framework. The Book Is Motivated By The Fact That Some Degree Of Uncertainty Is Inevitable In Characterizing Any Realistic Engineering Systems. Discussion Includes Representative Meth

[Lecture Notes in Computer Science] Appl
✍ Giacobini, Mario; Brabazon, Anthony; Cagnoni, Stefano; Di Caro, Gianni A.; Drech πŸ“‚ Article πŸ“… 2008 πŸ› Springer Berlin Heidelberg 🌐 English βš– 417 KB

Evolutionary computation (EC) techniques are e?cient, nature-inspired pl- ning and optimization methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these me- ods can be used in the context of problem solving, optimization, and ma