Book title: Swarm Intelligence Volume 3: Applications
Author: Dr Ying Tan (Ed.)
Product Code: PBCE119C
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The concept of swarm intelligence at first originated from the observation of nature. Through the observation and study of the behaviour of swarms of living creatures as ants colony, bird flocks, bees colony and fish school, inspired by the swarm/social phenomena exhibited by these biological swarms, the swarm of simple individuals through mutual cooperation shows up the emergence phenomena at the level of swarm, that is, "the swarm of simple individuals shows the characteristics of complex intelligent behaviour through cooperation.”
The swarm intelligence algorithms are characterised of simplicity, uncertainty, interactivity, distributed parallelism, robustness, scalability, and self-organization. At present, the study of swarm intelligence algorithms mainly includes theory, algorithm and application. Its development trends include developing hybrid algorithms, new improved algorithms and theoretical analysis as well as solving large-scale problems (big data application). In general, swarm intelligence algorithms may shed a light on breaking the curse of no free lunches (NFLs), which shows that a deep study might give us enough anticipation motivating more and more researchers to engage in the research of swarm intelligence algorithms and their applications.
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 get inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.
Swarm Intelligence Volume 3: Applications includes 27 chapters and presents a great number of real-world applications of swarm intelligence algorithms and related evolutionary algorithms.The companion volume I covers principles of swarm intelligence and volume II covers new algorithms and innovative methods of swarm intelligence.
You might also be interested in Swarm Intelligence Volume 1: Principles, current algorithms and methods, Swarm Intelligence Volume 2: Innovation, new algorithms and methods or Swarm Intelligence 3 Volume set.
Dr. Ying Tan is a Professor of Peking University 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 is the inventor of Fireworks Algorithm (FWA).
He serves as Editor-in-Chief of the International Journal of Computational Intelligence and Pattern Recognition, Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning Systems, the International Journal of Swarm Intelligence Research, and so on. He also served as guest editor of several referred Journals, including the IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, and Natural Computing, etc.
He has been the founder general chair of the ICSI International Conference series since 2010. He won many academic awards, including the 2nd-Class Natural Science Award of China in 2009, Outstanding Chapter Award by Springer, the Best Paper Award of CAAI Transactions on Intelligent Systems, and Innovative Achievement Award of CAAI in 2016. He has published more than 300 papers in refereed journals and conferences, and authored/co-authored 15 books and 30+ book chapters, and 4 invention patents.
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 working in the fields of computer science, information technology, artificial intelligence, neural networks, computational intelligence, bioengineering, physics, mathematics, and social sciences.
This information is provisional and will be updated prior to publication
Chapter 1: Prototype Generation Based on MOPSO - Weiwei Hu, Ying Tan
Chapter 2: Image Reconstruction Algorithms for Electrical Impedance Tomography based on Swarm Intelligence - Wellington Pinheiro dos Santos, Ricardo Emmanuel de Souza, Reiga Ramalho Ribeiro, Allan Rivalles Souza Feitosa, Valter Augusto de Freitas Barbosa, Victor Luiz Bezerra Araujo da Silva, David Edson Ribeiro, Rafaela Covello de Freitas
Chapter 3: A Semi-Supervised Fuzzy GrowCut Algorithm for Segmenting Masses of Regions of Interest of Mammography Images - Filipe R. Cordeiro, Wellington P. Santos, Abel G. Silva-Filho
Chapter 4: Multi-Objective Optimization of Autonomous Control for a Biped Robot - M. J. Mahmoodabadi, M. Taher Khorsandi, S.E. Rasouli
Chapter 5: Swarm Intelligence Based MIMO Detection Techniques in Wireless Systems - Adnan Ahmed Khan, Zakir Ullah
Chapter 6: Swarm Intelligence in Logistics and Production Planning - Thomas Hanne, Suash Deb, Simon Fong
Chapter 7: Swarm Intelligence for Object Based Image Analysis - Fatemeh Tabib Mahmoudi
Chapter 8: Evolutionary Multi-Objective Optimization for Multi-Label Learning - Chuan Shi
Chapter 9: Image Segmentation by Flocking-like Particle Dynamics - Roberto Alves Gueleri, Qiusheng Zheng, Junbao Zhang, Liang Zhao
Chapter 10: Swarm Intelligence for Controller Tuning and Control of Fractional Systems - Zafer Bingul, Oguzhan Karahan
Chapter 11: PSO-based implementation of smart antennas for secure localisation - Rathindra Nath Biswas, Anurup Saha, Swarup Kumar Mitra, Mrinal Kanti Naskar
Chapter 12: Evolutionary Computation for NLP Tasks - Ana Paula Silva, Arlindo Silva
Chapter 13: Particle Swarm Optimisation for Antenna Element Design - Waroth Kuhirun, Winyou Silabut, Pravit Boonek
Chapter 14: Swarm Intelligence for Data Mining Classification Tasks: an Experimental Study using Medical Decision Problems - Jose A. Saez, Emilio Corchado
Chapter 15: Towards Spiking Neutral Systems Synthesis - Abhaya C. Kammara S., S. Pontes-Filho, Andreas Konig
Chapter 16: Particle Swarm Optimization based Memetic Algorithms Framework for Scheduling of Central Planned and Distributed Flowshops - Yixin Yang, Xiaoyi Feng, Bin Xin, Mengchen Ji, Xiying Du, Ling Wang, Hongjun Zhang, Bo Liu
Chapter 17: Particle Swarm Optimization for Antenna Array Synthesis, Diagnosis and Healing - Om Prakash Acharya, Amalendu Patnaik
Chapter 18: Designing a Fuzzy Logic Controller with Particle Swarm Optimisation Algorithm - Gurcan Lokman, Vedat Topuz, Ahmet Fevzi Baba
Chapter 19: Adding Swarm Intelligence for Slope Stability Analysis - Walter W. Chen, Zhe- Ping Shen
Chapter 20: Software Module Clustering using Particle Swarm Optimization - Amarjeet, Jitender Kumar Chhabra
Chapter 21: A Swarm Intelligence approach to harness maximum Techno-Commercial Benefits from Smart Power Grids - Dr. Sandip Chanda, Dr. Abhinandan De
Chapter 22: Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation - Kamal Z. Zamli, Bestoun S. Ahmed, Thair Mahmoud, Wasif Afzal
Chapter 23: Multi-Objective Swarm Optimization for Operation Planning of Electric Power Systems - Rene Cruz Freire, Vitor Hugo Ferreira, Renan Silva Maciel
Chapter 24: Perturbed-Attractor Particle Swarm Optimization for Image Restoration - Deepak Devicharan, Kishan G. Mehrotra, Chilukuri K. Mohan, Pramod K. Varshney
Chapter 25: Application of Swarm Intelligence Algorithms to Multi-objective Distributed Local Area Network Topology Design Problem - Salman A. Khan, Amjad Mahmood
Chapter 26: Swarm Intelligence Algorithms for Antenna Design and Wireless Communications - Sotirios K. Goudos
Chapter 27: Finite element model updating using swarm intelligence algorithms - Ilyes Boulkaibet, Fernando Buarque de Lima Neto, Tshilidzi Marwala, Bhekisipho Twala