Skip to main content
Book cover

New Advancements in Swarm Algorithms: Operators and Applications

  • Book
  • © 2020

Overview

  • Presents advances in new, alternative swarm developments
  • Demonstrates the potential of new swarm alternative algorithms from a practical perspective
  • Discusses various novel metaheuristic methods and their practical applications

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 160)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (10 chapters)

Keywords

About this book

This book presents advances in alternative swarm development that have proved to be effective in several complex problems. Swarm intelligence (SI) is a problem-solving methodology that results from the cooperation between a set of agents with similar characteristics. The study of biological entities, such as animals and insects, manifesting social behavior has resulted in several computational models of swarm intelligence. While there are numerous books addressing the most widely known swarm methods, namely ant colony algorithms and particle swarm optimization, those discussing new alternative approaches are rare. The focus on developments based on the simple modification of popular swarm methods overlooks the opportunity to discover new techniques and procedures that can be useful in solving problems formulated by the academic and industrial communities. Presenting various novel swarm methods and their practical applications, the book helps researchers, lecturers, engineersand practitioners solve their own optimization problems.


Authors and Affiliations

  • CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas, Fernando Fausto, Adrián González

Bibliographic Information

Publish with us