Stigmergic Optimization / Edition 1

Stigmergic Optimization / Edition 1

by Ajith Abraham, Crina Grosan, Vitorino Ramos
     
 

ISBN-10: 3642071066

ISBN-13: 9783642071065

Pub. Date: 12/20/2010

Publisher: Springer Berlin Heidelberg

First studied in social insects like ants, indirect self-organizing interactions - known as "stigmergy" - occur when one individual modifies the environment and another subsequently responds to the new environment. The implications of self-organizing behavior extend to robotics and beyond. This book explores the application of stigmergy for a variety of

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Overview

First studied in social insects like ants, indirect self-organizing interactions - known as "stigmergy" - occur when one individual modifies the environment and another subsequently responds to the new environment. The implications of self-organizing behavior extend to robotics and beyond. This book explores the application of stigmergy for a variety of optimization problems. The volume comprises 12 chapters including an introductory chapter conveying the fundamental definitions, inspirations and research challenges.

Product Details

ISBN-13:
9783642071065
Publisher:
Springer Berlin Heidelberg
Publication date:
12/20/2010
Series:
Studies in Computational Intelligence Series, #31
Edition description:
Softcover reprint of hardcover 1st ed. 2006
Pages:
299
Product dimensions:
9.21(w) x 6.14(h) x 0.67(d)

Table of 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.- Shastic 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.

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