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Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals.
The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of swarm intelligence. Among the topics covered in this book include: particle swarm optimization and hybrid methods, ant colony optimization and hybrid methods, bee colony optimization, glowworm swarm optimization, and complex social swarms, application of various swarm intelligence models to operational planning of energy plants, modeling and control of nanorobots, classification of documents, identification of disease biomarkers, and prediction of gene signals.
The book is directed to researchers, practicing professionals, and undergraduate as well as graduate students of all disciplines who are interested in enhancing their knowledge in techniques and applications of swarm intelligence.
List of contents
Advances in Swarm Intelligence.- A Review of Particle Swarm Optimization Methods used for Multimodal Optimization.- Bee Colony Optimization (BCO).- Glowworm Swarm Optimization for Searching Higher Dimensional Spaces.- Agent Specialization in Complex Social Swarms.- Computational Complexity of Ant Colony Optimization and its Hybridization with Local Search.- A Multi-Resolution GA-PSO Layered Encoding Cascade Optimization Model.- Integrating Swarm Intelligent Algorithms for Translation Initiation Sites Prediction.- Particle Swarm Optimization for Optimal Operational Planning of Energy Plants.- Modelling Nanorobot Control using Swarm Intelligence: A Pilot Study.- ACO Hybrid Algorithm for Document Classification System.- Identifying Disease-Related Biomarkers by Studying Social Networks of Genes.
Summary
Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals.
The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of swarm intelligence. Among the topics covered in this book include: particle swarm optimization and hybrid methods, ant colony optimization and hybrid methods, bee colony optimization, glowworm swarm optimization, and complex social swarms, application of various swarm intelligence models to operational planning of energy plants, modeling and control of nanorobots, classification of documents, identification of disease biomarkers, and prediction of gene signals.
The book is directed to researchers, practicing professionals, and undergraduate as well as graduate students of all disciplines who are interested in enhancing their knowledge in techniques and applications of swarm intelligence.
