Advances in swarm intelligence for optimizing problems in computer science. (2018)

• Record Type: Book
• Further Information: Note: Edited by Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen.
• Editors: Nayyar, Anand; Le, Dac-Nhuong, 1983-; Nguyen, Nhu Gia
• Contents: 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 Optimiza
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2018
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 006.3824; Swarm intelligence; Computer algorithms
• Languages: English
• ISBNs: 9780429820151; 9780429820168; 9780429820144; 9780429445927
• Related ISBNs: 9781138482517
• Notes: Note: Description based on CIP data; resource not viewed.
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