Biologically-inspired optimisation methods: parallel algorithms, systems and applications
Lewis, A.
Mostaghim, S.
Randall, M.
Presents recent research in Biologically-inspired Optimisation Methods INDICE: Evolution’s Niche in Multi-Criterion Problem Solving.- Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimisation.- Asynchronous Multi-Objective Particle Swarm Optimisation in Unreliable Distributed Environments.- Dynamic Problems and Nature Inspired Meta-heuristics.-Relaxation Labelling Using Distributed Neural Networks.- Extremal Optimisation for Assignment Type Problems.- Niching for Ant Colony Optimisation.- Using Ant Colony Optimisation to Improve Small Meander Line RFID Antennas.- The RadioNetwork Design Optimisation Problem and State-of-the-Art Solvers.- Parallel Evolutionary Algorithms for Urban Energy Management.- An Analysis of Dynamic Operators for Conformational Sampling on Grids.- Evolving Computer Chinese ChessUsing Guided Learning.
- ISBN: 978-3-642-01261-7
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 360
- Fecha Publicación: 01/06/2009
- Nº Volúmenes: 1
- Idioma: Inglés