Book Description:
Swarm intelligence is an innovative computational way to solve hard problems. This discipline is inspired by the behavior of social insect (e.g. ants, termites, bees and wasps) colonies as well as fish schools and bird flocks. The content of this book is divided into two main parts: ant colony optimization and particle swarm optimization. The former describes systems that have been engineered using ant colony optimization and the latter presents systems that were designed based on particle swarm optimization.

Table of Contents:
Part I. Ant Colony Optimization:

Chapter 1. An Ant Colony Optimization Meta-Heuristic for Subset Selection Problems pp. 3-25
(Christine Solonon, University of Lyon, France and Derek Bridge, University College Cork, Ireland)


Chapter 2. Ant Colony System for the Beam Angle Optimization Problem in Radiotherapy Planning pp. 27-51
(Yingjie Li and Dezhong Yao, University of Electronic Science and Technology, China)

Chaper 3. Ant Colony Optimisation for Fast Exponentiations Using Optimal Addition-Subtraction Chains pp. 53-63
(Nadia Nedjah and Luiza de Macedo Mourelle)

Part II Particle Swarm Optimization:

Chapter 4. Comparative Investigation of PSO vs. GA for Dynamically Reconfigurable Sensor Electronics pp. 67-84
(Peter Tawdross and Andreas König, University of Kaiserslautern, Germay)


Chapter 5. Piecewise Reference-driven Takagi-Sugeno Fuzzy Modelling based on Particle Swarm Optimization pp. 85-112
(Ernesto Araujo, Instituto Nacional de Pesquisas Espaciais, Brazil and Leandro do Santos Coelho, Pontifcia Universidade Catlica do Paran, Brazil)


Chapter 6. Diffusion Controlled Cellular Automaton Performing Mesh Partitioning with Dynamic Inserting of Domains pp. 113-130
(Jiøí Kroc, Helsinki School of Economics, Finland)


Index