ShopSpell

Multi-objective Swarm Intelligence Theoretical Advances and Applications [Paperback]

$78.99     $109.99    28% Off      (Free Shipping)
100 available
  • Category: Books (Computers)
  • ISBN-10:  3662523655
  • ISBN-10:  3662523655
  • ISBN-13:  9783662523650
  • ISBN-13:  9783662523650
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Apr-2016
  • Pub Date:  01-Apr-2016
  • SKU:  3662523655-11-SPRI
  • SKU:  3662523655-11-SPRI
  • Item ID: 100979682
  • List Price: $109.99
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jul 04 to Jul 06
  • Notes: Brand New Book. Order Now.

The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.???????

Introduction.- Behavior of Bacterial Colony.- E.coli Bacterial Colonies.- Optimization based on E.coli Bacterial Colony.- Classification of BFO Algorithm.- Multi-objective optimization based on BF.- An overview of BFO Applications.- Conclusion.

The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 coverló,

Add Review