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基本説明
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.
Full Description
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.
Contents
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.
