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| 010 | ▼a 2018020199 | |
| 020 | ▼a 9781138482517 (hardback : alk. paper) | |
| 020 | ▼a 9780429445927 (ebook) | |
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| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
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| 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 |
컨텐츠정보
책소개
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.
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.
정보제공 :
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