Swarm Intelligence: Innovation, new algorithms and methods

by Ying Tan (Editor)

Overview

Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.

Volume 2 includes 17 chapters covering front-edge research with novel and newly proposed algorithms and methods.

With contributions from an international selection of leading researchers, Swarm Intelligence is essential reading for engineers, researchers, professionals and practitioners with interests in swarm intelligence.

Product Details

ISBN-13:	9781785616297
Publisher:	The Institution of Engineering and Technology
Publication date:	11/30/2018
Series:	Control, Robotics and Sensors
Pages:	544
Product dimensions:	6.14(w) x 9.21(h) x (d)

About the Author

Ying Tan is a full professor, PhD advisor, and director of the Computational Intelligence Laboratory at Peking University, China. He is also a professor at the Faculty of Design, Kyushu University, Japan. He serves as Editor-in-Chief of the International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), and is Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), IEEE Transactions on Neural Networks and Learning Systems (NNLS), International Journal of Swarm Intelligence Research (IJSIR), and International Journal of Artificial Intelligence (IJAI). He has been the founder general chair of the ICSI International Conference series since 2010, is the inventor of the Fireworks Algorithm (FWA), and has published extensively in this field.

Table of Contents

• Chapter 1: Standard fireworks algorithm 2017
• Chapter 2: Guided fireworks algorithm applied to multilevel image thresholding
• Chapter 3: Credit card number encryption using firework-based key generation
• Chapter 4: ST (Shafiabady-Teshnehlab) optimization algorithm
• Chapter 5: Predator-prey optimization with heterogeneous swarms
• Chapter 6: A novel modified ant lion optimizer algorithm: extension to proposed 4D-TC
• Chapter 7: Push-pull glowworm swarm optimization algorithm for multimodal functions
• Chapter 8: Firefly algorithm and its applications
• Chapter 9: The optimization dialectical method for the multiple sequences alignment problem
• Chapter 10: A new binary moth-flame optimization algorithm (BMFOA) - development and application to solve unit commitment problem
• Chapter 11: Binary whale optimization algorithm for unit commitment problem in power system operation
• Chapter 12: Real-coded grey wolf optimisation algorithm for progressive thermal power system unit commitment
• Chapter 13: Application of grey wolf optimization in fuzzy controller tuning for servo systems
• Chapter 14: Smart swarm inspired algorithms for microwave imaging problems
• Chapter 15: Interactive chaotic evolution
• Chapter 16: Symbiotic organisms search algorithm for static and dynamic transmission expansion planning
• Chapter 17: Inclined planes system optimisation (IPO) and its applications in data mining and system identification