HOME > 상세정보

상세정보

Advances in swarm intelligence for optimizing problems in computer science

Advances in swarm intelligence for optimizing problems in computer science

자료유형
단행본
개인저자
Nayyar, Anand. Le, Dac-Nhuong, 1983-. Nguyen, Nhu Gia.
서명 / 저자사항
Advances in swarm intelligence for optimizing problems in computer science / edited by Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen.
발행사항
Boca Raton, FL :   CRC Press/Taylor & Francis Group,   c2019.  
형태사항
xiv, 298 p. : ill. ; 25 cm.
ISBN
9781138482517 (hardback : alk. paper) 9780429445927 (ebook)
요약
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 foundation for authors, researchers and industry professionals--
서지주기
Includes bibliographical references and index.
일반주제명
Swarm intelligence. Computer algorithms.
000 00000cam u2200205 a 4500
001 000046007425
005 20191206104928
008 191126s2019 flua b 001 0 eng d
010 ▼a 2018020199
020 ▼a 9781138482517 (hardback : alk. paper)
020 ▼a 9780429445927 (ebook)
035 ▼a (KERIS)REF000018714508
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
050 0 0 ▼a Q337.3 ▼b .A375 2019
082 0 4 ▼a 006.3 ▼2 23
082 0 0 ▼a 006.3/824 ▼2 23
084 ▼a 006.3 ▼2 DDCK
090 ▼a 006.3 ▼b A2447
245 0 0 ▼a Advances in swarm intelligence for optimizing problems in computer science / ▼c edited by Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen.
260 ▼a Boca Raton, FL : ▼b CRC Press/Taylor & Francis Group, ▼c c2019.
300 ▼a xiv, 298 p. : ▼b ill. ; ▼c 25 cm.
504 ▼a Includes bibliographical references and index.
520 ▼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 foundation for authors, researchers and industry professionals-- ▼c Provided by publisher.
650 0 ▼a Swarm intelligence.
650 0 ▼a Computer algorithms.
700 1 ▼a Nayyar, Anand.
700 1 ▼a Le, Dac-Nhuong, ▼d 1983-.
700 1 ▼a Nguyen, Nhu Gia.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.3 A2447 등록번호 121251188 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Contents





Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii


Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xv





1. Evolutionary Computation: Theory and Algorithms . . . . . . . . . . . . . . . .1


Anand Nayyar, Surbhi Garg, Deepak Gupta, and Ashish Khanna





1.1 History of Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . .2


1.2 Motivation via Biological Evidence . . . . . . . . . . . . . . . . . . . . . . . . .3


1.3 Why Evolutionary Computing?. . . . . . . . . . . . . . . . . . . . . . . . . . . .5


1.4 Concept of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . .6


1.5 Components of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . .9


1.6 Working of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . .13


1.7 Evolutionary Computation Techniques and Paradigms. . . . . . .15


1.8 Applications of Evolutionary Computing . . . . . . . . . . . . . . . . . .21


1.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23





2. Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27


Sandeep Kumar, Sanjay Jain, and Harish Sharma





2.1 Overview of Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . .27


2.2 Genetic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32


2.3 Derivation of Simple Genetic Algorithm . . . . . . . . . . . . . . . . . . .39


2.4 Genetic Algorithms vs. Other Optimization Techniques . . . . . .43


2.5 Pros and Cons of Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . .45


2.6 Hybrid Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45


2.7 Possible Applications of Computer Science via Genetic


Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46


2.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48





3. Introduction to Swarm Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53


Anand Nayyar and Nhu Gia Nguyen





3.1 Biological Foundations of Swarm Intelligence . . . . . . . . . . . . . . .53


3.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56


3.3 Concept of Swarm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62


3.4 Collective Intelligence of Natural Animals. . . . . . . . . . . . . . . . . .64


3.5 Concept of Self-Organization in Social Insects. . . . . . . . . . . . . . .68


3.6 Adaptability and Diversity in Swarm Intelligence . . . . . . . . . . .70


3.7 Issues Concerning Swarm Intelligence . . . . . . . . . . . . . . . . . . . . .71


3.8 Future Swarm Intelligence in Robotics - Swarm Robotics . . . . .73


3.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75





4. Ant Colony Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .79


Bandana Mahapatra and Srikanta Pattnaik





4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .80


4.2 Concept of Artificial Ants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81


4.3 Foraging Behaviour of Ants and Estimating Effective Paths . . . 83


4.4 ACO Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87


4.5 ACO Applied Toward Travelling Salesperson Problem. . . . . . .91


4.6 ACO Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93


4.7 The Ant Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95


4.8 Comparison of Ant Colony Optimization Algorithms . . . . . . . .97


4.9 ACO for NP Hard Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . .102


4.10 Current Trends in ACO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105


4.11 Application of ACO in Different Fields . . . . . . . . . . . . . . . . . . .106


4.12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109





5. Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .115


Shanthi M.B., D. Komagal Meenakshi, and Prem Kumar Ramesh





5.1 Particle Swarm Optimization - Basic Concepts . . . . . . . . . . . . .116


5.2 PSO Variants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118


5.3 Particle Swarm Optimization (PSO) - Advanced Concepts . . . 134


5.4 Applications of PSO in Various Engineering Domains. . . . . . .139


5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .141


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141





6. Artificial Bee Colony, Firefly Swarm Optimization, and Bat


Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145


Sandeep Kumar and Rajani Kumari





6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146


6.2 The Artificial Bee Colony Algorithm. . . . . . . . . . . . . . . . . . . . . .147


6.3 The Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .163


6.4 The Bat Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .170


x Contents


6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .177


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178





7. Cuckoo Search Algorithm, Glowworm Algorithm,


WASP, and Fish Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . .183


Akshi Kumar





7.1 Introduction to Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . .184


7.2 Cuckoo Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .186


7.3 Glowworm Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .200


7.4 Wasp Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . .208


7.5 Fish Swarm Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213


7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .221


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221





8. Misc. Swarm Intelligence Techniques . . . . . . . . . . . . . . . . . . . . . . . . . .225


M. Balamurugan, S. Narendiran, and Sarat Kumar Sahoo





8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .226


8.2 Termite Hill Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .227


8.3 Cockroach Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . .230


8.4 Bumblebee Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .232


8.5 Social Spider Optimization Algorithm . . . . . . . . . . . . . . . . . . . .234


8.6 Cat Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .237


8.7 Monkey Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .239


8.8 Intelligent Water Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241


8.9 Dolphin Echolocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .242


8.10 Biogeography-Based Optimization . . . . . . . . . . . . . . . . . . . . . . .244


8.11 Paddy Field Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .247


8.12 Weightless Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . .248


8.13 Eagle Strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .249


8.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .250


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251





9. Swarm Intelligence Techniques for Optimizing Problems. . . . . . . . .253


K. Vikram and Sarat Kumar Sahoo





9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .253


9.2 Swarm Intelligence for Communication Networks. . . . . . . . . .254


9.3 Swarm Intelligence in Robotics . . . . . . . . . . . . . . . . . . . . . . . . . .257


9.4 Swarm Intelligence in Data Mining. . . . . . . . . . . . . . . . . . . . . . .261


9.5 Swarm Intelligence and Big Data. . . . . . . . . . . . . . . . . . . . . . . . .264


9.6 Swarm Intelligence in Artificial Intelligence (AI) . . . . . . . . . . .268


9.7 Swarm Intelligence and the Internet of Things (IoT). . . . . . . . .270


9.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .273


References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

관련분야 신착자료

National Academies of Sciences, Engineering, and Medicine (U.S.) (2020)
Cartwright, Hugh M. (2021)
한국소프트웨어기술인협회. 빅데이터전략연구소 (2021)