Advances in Swarm Intelligence for Optimizing Problems in Computer Science / edited by Anand Nayyar, Dac-Nhuong Le and Nhu Gia Nguyen.

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$a 9780429445927 $q (e-book : PDF)

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$a Advances in Swarm Intelligence for Optimizing Problems in Computer Science / $c edited by Anand Nayyar, Dac-Nhuong Le and Nhu Gia Nguyen.

$a First edition.

$a Boca Raton, FL : $b Chapman and Hall/CRC, $c [2018].

$c ©2019.

$a 1 online resource (314 pages) : $b 54 illustrations, text file, PDF

$a text $2 rdacontent

$a computer $2 rdamedia

$a online resource $2 rdacarrier

$a Includes bibliographical references and index.

$t Contents -- $t Contributors  xiii -- $t Preface  xv -- $t 1. Evolutionary Computation: Theory and Algorithm-- $t Anand Nayyar, Surbhi Garg, Deepak Gupta, and Ashish Khanna -- $t 1.1 History of Evolutionary Computatio-- $t 1.2 Motivation via Biological Evidenc.3 -- $t 1.3 Why Evolutionary Computing?.5 -- $t 1.4 Concept of Evolutionary Algorithms  .6 -- $t 1.5 Components of Evolutionary Algorithm-- $t 1.6 Working of Evolutionary Algorithm. .13 -- $t 1.7 Evolutionary Computation Techniques and Paradigms.15 -- $t 1.8 Applications of Evolutionary Computin . .21 -- $t 1.9 Conclusio.23 -- $t Reference23 -- $t 2. Genetic Algorithm.27 -- $t Sandeep Kumar, Sanjay Jain, and Harish Sharma -- $t 2.1 Overview of Genetic Algorithms  .27 -- $t 2.2 Genetic Optimization.32 -- $t 2.3 Derivation of Simple Genetic Algorith.39 -- $t 2.4 Genetic Algorithms vs. Other Optimization Technique.43 -- $t 2.5 Pros and Cons of Genetic Algorithms.45 -- $t 2.6 Hybrid Genetic Algorithm.45 -- $t 2.7 Possible Applications of Computer Science via Genetic -- $t Algorithm.46 -- $t 2.8 Conclusio.47 -- $t Reference48 -- $t 3. Introduction to Swarm Intelligence.53 -- $t Anand Nayyar and Nhu Gia Nguyen -- $t 3.1 Biological Foundations of Swarm Intelligenc.53 -- $t 3.2 Metaheuristic. .56 -- $t 3.3 Concept of Swar.62 -- $t 3.4 Collective Intelligence of Natural Animals.64 -- $t 3.5 Concept of Self-Organization in Social Insects.68 -- $t 3.6 Adaptability and Diversity in Swarm Intelligenc. .70 -- $t 3.7 Issues Concerning Swarm Intelligenc. .71 -- $t 3.8 Future Swarm Intelligence in Robotics  Swarm Robotic.73 -- $t 3.9 Conclusio.75 -- $t Reference75 -- $t 4. Ant Colony Optimizatio.79 -- $t Bandana Mahapatra and Srikanta Pattnaik -- $t 4.1 Introductio.80 -- $t 4.2 Concept of Artificial Ant. .81 -- $t 4.3 Foraging Behaviour of Ants and Estimating Effective Path83 -- $t 4.4 ACO Metaheuristic.87 -- $t 4.5 ACO Applied Toward Travelling Salesperson Problem.91 -- $t 4.6 ACO Framewor.93 -- $t 4.7 The Ant Algorith.95 -- $t 4.8 Comparison of Ant Colony Optimization Algorithm.97 -- $t 4.9 ACO for NP Hard Problems.102 -- $t 4.10 Current Trends in ACO. .105 -- $t 4.11 Application of ACO in Different Field.106 -- $t 4.12 Conclusio.109 -- $t Reference109 -- $t 5. Particle Swarm Optimizatio.115 -- $t Shanthi M.B., D. Komagal Meenakshi, and Prem Kumar Ramesh -- $t 5.1 Particle Swarm Optimization  Basic Concept.116 -- $t 5.2 PSO Variants.118 -- $t 5.3 Particle Swarm Optimization (PSO)  Advanced Concept134 -- $t 5.4 Applications of PSO in Various Engineering Domains.139 -- $t 5.5 Conclusio.141 -- $t Reference141 -- $t 6. Artificial Bee Colony, Firefly Swarm Optimization, and Bat -- $t Algorithm.145 -- $t Sandeep Kumar and Rajani Kumari -- $t 6.1 Introductio. .146 -- $t 6.2 The Artificial Bee Colony Algorithm.147 -- $t 6.3 The Firefly Algorith.163 -- $t 6.4 The Bat Algorith.170 -- $t x Contents -- $t 6.5 Conclusio.177 -- $t Reference178 -- $t 7. Cuckoo Search Algorithm, Glowworm Algorithm, -- $t WASP, and Fish Swarm Optimizatio.183 -- $t Akshi Kumar -- $t 7.1 Introduction to Optimizatio.184 -- $t 7.2 Cuckoo Searc.186 -- $t 7.3 Glowworm Algorithm. .200 -- $t 7.4 Wasp Swarm Optimizatio.208 -- $t 7.5 Fish Swarm Optimization.213 -- $t 7.6 Conclusio.221 -- $t Reference221 -- $t 8. Misc. Swarm Intelligence Technique.225 -- $t M. Balamurugan, S. Narendiran, and Sarat Kumar Sahoo -- $t 8.1 Introductio. .226 -- $t 8.2 Termite Hill Algorith.227 -- $t 8.3 Cockroach Swarm Optimizatio.230 -- $t 8.4 Bumblebee Algorith.232 -- $t 8.5 Social Spider Optimization Algorith.234 -- $t 8.6 Cat Swarm Optimizatio. .237 -- $t 8.7 Monkey Search Algorith.239 -- $t 8.8 Intelligent Water Dro.241 -- $t 8.9 Dolphin Echolocatio.242 -- $t 8.10 Biogeography-Based Optimizatio.244 -- $t 8.11 Paddy Field Algorith.247 -- $t 8.12 Weightless Swarm Algorith.248 -- $t 8.13 Eagle Strategy.249 -- $t 8.14 Conclusio.250 -- $t Reference251 -- $t 9. Swarm Intelligence Techniques for Optimizing Problems.253 -- $t K. Vikram and Sarat Kumar Sahoo -- $t 9.1 Introductio. .253 -- $t 9.2 Swarm Intelligence for Communication Networks.254 -- $t 9.3 Swarm Intelligence in Robotic.257 -- $t 9.4 Swarm Intelligence in Data Mining. .261 -- $t 9.5 Swarm Intelligence and Big Data.264 -- $t 9.6 Swarm Intelligence in Artificial Intelligence (AI.268 -- $t 9.7 Swarm Intelligence and the Internet of Things (IoT.270 -- $t 9.8 Conclusio.273 -- $t Reference273 -- $t Inde.274.

$a This book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs. It will act as a foundation for authors, researchers and industry professionals. This monograph will present the latest state of the art research being done on varied Intelligent Technologies like sensor networks, machine learning, optical fiber communications, digital signal processing, image processing and many more.

$a Also available in print format.

$a Swarm intelligence.

$a MATHEMATICS / General. $2 bisacsh

$a TECHNOLOGY & ENGINEERING / Electronics / General. $2 bisacsh

$a Computer Based Optimization Techniques. $2 bisacsh

$a Evolutionary Computation. $2 bisacsh

$a Nature Based Computing. $2 bisacsh

$a Electronic books.

$a Nayyar, Anand, $e editor.

$a Le, Dac-Nhuong, $e editor.

$a Nguyen, Nhu Gia, $e editor.

$a Taylor and Francis.

$i Print version: $z 9781138482517

$u https://www.taylorfrancis.com/books/9780429445927 $z Click here to view.

$a VIRTUA