Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi'an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

1. Bio-Inspired Computation and Optimization: An Overview 2. Bio-inspired Approaches in Telecommunications Firefly Algorithm in Telecommunications 3. A Survey of Intrusion Detection Systems Using Evolutionary Computation 4. VoIP Quality Prediction Model by Bio-inspired Methods, On the impact of Differential Evolution parameters solving the Survivable Virtual Topology Mapping Problem in IP-over-WDM Networks, Radio Resource Management by Evolutionary Algorithms for 4G LTE-Advanced Networks 5. Robust Transmission for Heterogeneous Networks with Cognitive Small Cells 6. Ecologically-inspired Resource Distribution Techniques for Sustainable Communication Networks 7. Multi-objective Optimization in Optical Networks 8. Cell Coverage Area Optimization based on Particle Swarm Optimization (PSO) for Green Macro Long-term Evolution (LTE) Cellular Network 9. Bio-Inspired Computation for Solving the Optimal Coverage Problem in Wireless Sensor Networks: A Binary Particle Swarm Optimization Approach 10. Clonal Selection based Minimum Interference Channel Assignment Algorithms for Multi-radio Wireless Mesh Networks