Swarm Intelligence

8th International Conference, ANTS 2012, Brussels, Belgium, September 12-14, 2012, Proceedings

Häftad, Engelska, 2012

Av Mauro Birattari, Christian Blum, Anders Lyhne Christensen, Andries P. Engelbrecht, Roderich Groß, Marco Dorigo, Thomas Stützle, Thomas Stutzle

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This book constitutes the proceedings of the 8th International Conference on Swarm Intelligence, held in Brussels, Belgium, in September 2012.This volume contains 15 full papers, 20 short papers, and 7 extended abstracts carefully selected out of 81 submissions. The papers cover various topics of swarm intelligence.

Produktinformation

Utgivningsdatum2012-08-07
Mått155 x 235 x 21 mm
Vikt563 g
FormatHäftad
SpråkEngelska
SerieTheoretical Computer Science and General Issues
Antal sidor356
Upplaga2012
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642326493

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Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Programmeringsböcker inom Data och it
Artificiell intelligens inom Data och it

A Particle Swarm Embedding Algorithm for Nonlinear Dimensionality.- ABC-Miner: An Ant-Based Bayesian Classification Algorithm.- Analysing Robot Swarm Decision-Making with Bio-PEPA.- Automatic Generation of Multi-objective ACO Algorithms for the Bi-objective Knapsack.- Bare Bones Particle Swarms with Jumps.- Hybrid Algorithms for the Minimum-Weight Rooted Arborescence Problem.- Improving the cAnt-MinerPB Classification Algorithm.- Introducing Novelty Search in Evolutionary Swarm Robotics.- Measuring Diversity in the Cooperative Particle Swarm Optimizer.- Multi-armed Bandit Formulation of the Task Partitioning Problem in Swarm Robotics.- Scalability Study of Particle Swarm Optimizers in Dynamic Environments.- Self-reconfigurable Modular e-pucks.- Task Partitioning via Ant Colony Optimization for Distributed Assembly.- The Self-adaptive Comprehensive Learning Particle Swarm Optimizer.- Towards Swarm Calculus: Universal Properties of Swarm Performance and Collective Decisions.- A Hybrid Particle Swarm Optimization Algorithm for the Open Vehicle Routing Problem.- A Self-adaptive Heterogeneous PSO Inspired by Ants.- A“Thermodynamic”Approach to Multi-robot Cooperative Localization with Noisy Sensors.- AcoSeeD: An Ant Colony Optimization for Finding Optimal Spaced Seeds in Biological Sequence Search.- Analysis of Ant-Based Routing with Wireless Medium Access Control.- Ant-Based Approaches for Solving Autocorrelation Problems.- Collision-Induced “Priority Rule” Governs Efficiency of Pheromone-Communicating Swarm Robots.- Dynamic Load Balancing Inspired by Cemetery Formation in Ant Colonies.- Feasibility of an Ant Colony Optimization Algorithm for Multi-leaf Collimator (MLC) Aperture Definition and Beam Weighting in Volumetric Modulated Arc Therapy (VMAT) Radiotherapy Treatment Planning.- Ant Swarm Foraging from Physical to Virtualand Back Again.- Improving Peer Review with ACORN: ACO Algorithm for Reviewer’s Network.- Learning Finite-State Machines with Ant Colony Optimization.- Mobbing Behavior and Deceit and Its Role in Bio-inspired Autonomous Robotic Agents.- Performance of Bacterial Foraging Optimization in Dynamic Environments.- Piecewise Linear Approximation of n-Dimensional Parametric Curves Using Particle Swarms.- Probabilistic Stochastic Diffusion Search.- Self-organized Clustering of Square Objects by Multiple Robots.- Self-reproduction versus Transition Rules in Ant Colonies for Medical Volume Segmentation .- Swarm Interpolation Using an Approximate Chebyshev Distribution.- Using MOPSO to Solve Multiobjective Bilevel Linear Problems.- Clustering Moodle Data via Ant Colony Optimization .- Continuous Trait-Based Particle Swarm Optimisation (CTB-PSO).- Exploring Different Functions for Heuristics, Discretization, and Rule Quality Evaluation in Ant-Miner .- Fuzzy-Based Aggregation with a Mobile Robot Swarm.- Maturity of the Particle Swarm as a Metric for Measuring the Particle Swarm Intelligence .- Multi-objective Firefly Algorithm for Energy Optimization in Grid Environments .- Particle Swarm Optimization with Random Sampling in Variable Neighbourhoods for Solving Global Minimization Problems.