Stigmergic Optimization

Biologists studied the behavior of social insects for a long time. After millions of years of evolution all these species have developed incredible solutions for a wide range of problems. The intelligent solutions to problems naturally emerge from the self-organization and indirect communication of these individuals. Indirect interactions occur between two individuals when one of them modifies the environment and the other responds to the new environment at a later time. Such an interaction is an example of 'stigmergy'. This book deals with the application of stigmergy for a variety of optimization problems. This volume comprises 12 chapters including an introductory chapter giving the fundamental definitions, inspirations and some research challenges.

Important features include a detailed overview of all the stigmergic optimization paradigms, excellent coverage of timely, advanced stigmergic optimization topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and application of stigmergic optimization will find the comprehensive coverage of this book invaluable.

Reports recent research results on Stigmergic Organization

Autorentext
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Inhalt
Stigmergic Optimization: Inspiration, Technologies and Perspectives.- Stigmergic Autonomous Navigation in Collective Robotics.- A General Approach to Swarm Coordination using Circle Formation.- Stigmergic Navigation for Multi-Agent Teams in Complex Environments.- Physically Realistic Self-assembly Simulation System.- Gliders and Riders: A Particle Swarm Selects for Coherent Space-Time Structures in Evolving Cellular Automata.- Termite: A swarm intelligent routing algorithm for mobilewireless Ad-Hoc networks.- Stochastic Diffusion Search: Partial Function Evaluation In Swarm Intelligence Dynamic Optimisation.- Linear Multi-Objective Particle Swarm Optimization.- Cooperative Particle Swarm Optimizers: A Powerful and Promising Approach.- Parallel Particle Swarm Optimization Algorithms with Adaptive Simulated Annealing.- Swarm Intelligence: Theoretical Proof That Empirical Techniques are Optimal.