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• Introduction.- Ant Colony Optimization.- Multiple Knapsack Problem.- Grid Scheduling Problem.- ACO for GPS Surveying Problem.- ACO for E. coli Cultivation Model.- ACO for Wireless Sensor Network Positioning.
• (source: Nielsen Book Data)

This book is interesting and full of new ideas. It provokes the curiosity of the readers. The book targets both researchers and practitioners. The students and the researchers will acquire knowledge about ant colony optimization and its possible applications as well as practitioners will find new ideas and solutions of their combinatorial optimization and decision-making problems. Ant colony optimization is between the best method for solving difficult optimization problems arising in real life and industry. It has obtained distinguished results on some applications with very restrictive constraints. The reader will find theoretical aspects of ant method as well as applications on a variety of problems. The following applications could be mentioned: multiple knapsack problem, which is an important economical problem; grid scheduling problem; GPS surveying problem; E. coli cultivation modeling; wireless sensor network positioning; image edges detection; workforce planning.
(source: Nielsen Book Data)

• A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
• A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
• An Improved ACO Based Plug-in to Enhance the Interpretability of Fuzzy Rule Bases with Exceptions
• Ant Colony Optimization for Energy-Efficient Broadcasting in Ad-Hoc Networks
• Ant Colony Optimization for Genome-Wide Genetic Analysis
• cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
• Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics
• Gathering Multiple Robotic Agents with Crude Distance Sensing Capabilities
• Integration of ACO in a Constraint Programming Language
• Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems
• Modeling Phase Transition in Self-organized Mobile Robot Flocks
• Molecular Structure Elucidation Using Ant Colony Optimization: A Preliminary Study
• Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
• Simple Dynamic Particle Swarms without Velocity
• Swarming in a Virtual World: A PSO Approach to Virtual Camera Composition
• The Binary Bridge Selection Problem: Stochastic Approximations and the Convergence of a Learning Algorithm
• Two-Level ACO for Haplotype Inference Under Pure Parsimony
• What Hides in Dimension X? A Quest for Visualizing Particle Swarms
• Short Papers
• A Dynamic Swarm for Visual Location Tracking
• A Simulation Study of Routing Performance in Realistic Urban Scenarios for MANETs
• ACO-Based Scheduling of Parallel Batch Processing Machines with Incompatible Job Families to Minimize Total Weighted Tardiness
• Adaptive Particle Swarm Optimization
• Ant Based Heuristics for the Capacitated Fixed Charge Location Problem
• Ant Colony Optimization and the Single Round Robin Maximum Value Problem
• Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns
• Autonomous Reconfiguration in a Self-assembling Multi-robot System
• Beanbag Robotics: Robotic Swarms with 1-DoF Units
• BlåtAnt: Bounding Networks' Diameter with a Collaborative Distributed Algorithm
• Dependency by Concentration of Pheromone Trail for Multiple Robots
• Dissemination of Information with Fair Load Distribution in Self-organizing Grids
• Emergent Sorting in Networks of Router Agents
• Enhancing the Cooperative Transport of Multiple Objects
• Formal Modeling of BeeAdHoc: A Bio-inspired Mobile Ad Hoc Network Routing Protocol
• Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series
• Incorporating Preferences to a Multi-objective Ant Colony Algorithm for Time and Space Assembly Line Balancing
• KANTS: Artifical Ant System for Classification
• Lattice Formation in Space for a Swarm of Pico Satellites
• Merging Groups for the Exploration of Complex State Spaces in the CPSO Approach
• Parallel Ant Colony Optimization for the Quadratic Assignment Problems with Symmetric Multi Processing
• Social Odometry in Populations of Autonomous Robots
• The Architecture of Ant-Based Clustering to Improve Topographic Mapping
• The Small World of Pheromone Trails
• Extended Abstracts
• A Particle Swarm Optimization Algorithm for Multiuser Scheduling in HSDPA
• AntLib v1.0: A Generic C++ Framework for Ant Colony Optimization
• Applying a Distributed Swarm-Based Algorithm to Solve Instances of the RCPSP
• bicACO: An Ant Colony Inspired Biclustering Algorithm
• Dynamic Routing and Travel Time Prediction with Ant Based Control
• Network Formation Using Ant Colony Optimization
• On the Stability and the Parameters of Particle Swarm Optimization
• Regional Traffic Assignment by ACO
• SwarmClass: A Novel Data Clustering Approach by a Hybridization of an Ant Colony with Flying Insects
• The Differential Ant-Stigmergy Algorithm for Large Scale Real-Parameter Optimization.

This book constitutes the refereed proceedings of the 6th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, held in Brussels, Belgium, in September 2008. The 17 revised full papers, 24 revised short papers, and 10 extended abstracts presented were carefully reviewed and selected from 91 submissions. The papers cover theoretical and foundational aspects of computational intelligence and related disciplines with special focus on swarm intelligence and are devoted to behavioral models of social insects and new algorithmic approaches, empirical and theoretical research in swarm intelligence, applications such as ant colony optimization or particle swarm optimization, and theoretical and experimental research in swarm robotics systems.
(source: Nielsen Book Data)

• Introduction.- State of the Art.- Problem Formulation and Optimization Approach.- MTS Algorithms for Cardinal UAV Motion Models.
• (source: Nielsen Book Data)

This book proposes some novel approaches for finding unmanned aerial vehicle trajectories to reach targets with unknown location in minimum time. At first, it reviews probabilistic search algorithms that have been used for dealing with the minimum time search (MTS) problem, and discusses how metaheuristics, and in particular the ant colony optimization algorithm (ACO), can help to find high-quality solutions with low computational time. Then, it describes two ACO-based approaches to solve the discrete MTS problem and the continuous MTS problem, respectively. In turn, it reports on the evaluation of the ACO-based discrete and continuous approaches to the MTS problem in different simulated scenarios, showing that the methods outperform in most all the cases over other state-of-the-art approaches. In the last part of the thesis, the work of integration of the proposed techniques in the ground control station developed by Airbus to control ATLANTE UAV is reported in detail, providing practical insights into the implementation of these methods for real UAVs.
(source: Nielsen Book Data)

• Heuristics I.- Alternative Solution Representations for the Job Shop Scheduling Problem in Ant Colony Optimisation.- Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search.- Concealed Contributors to Result Quality - The Search Process of Ant Colony System.- Ants Guide Future Pilots.- Complex Systems I.- Information Transfer by Particles in Cellular Automata.- An Artificial Development Model for Cell Pattern Generation.- Rounds Effect in Evolutionary Games.- Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction.- An Evolutionary Benefit from Misperception in Foraging Behaviour.- Simulated Evolution of Discourse with Coupled Recurrent Networks.- How Different Hierarchical Relationships Impact Evolution.- A Dual Phase Evolution Model of Adaptive Radiation in Landscapes.- Biological Systems I.- Directed Evolution of an Artificial Cell Lineage.- An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity.- Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS).- Self-maintained Movements of Droplets with Convection Flow.- Structural Circuits and Attractors in Kauffman Networks.- The Effects of Learning on the Roles of Chance, History and Adaptation in Evolving Neural Networks.- Unsupervised Acoustic Classification of Bird Species Using Hierarchical Self-organizing Maps.- The Prisoner's Dilemma with Image Scoring on Networks: How Does a Player's Strategy Depend on Its Place in the Social Network?.- Heuristics II.- Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation.- Mechanisms for Evolutionary Reincarnation.- An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization.- Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.- Complex Systems II.- A Framework for the Co-evolution of Genes, Proteins and a Genetic Code Within an Artificial Chemistry Reaction Set.- In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior.- A Principled Approach to Swarm-Based Wall-Building.- Pattern Extraction Improves Automata-Based Syntax Analysis in Songbirds.- Heuristics III.- A Modified Strategy for the Constriction Factor in Particle Swarm Optimization.- A Differential Evolution Variant of NSGA II for Real World Multiobjective Optimization.- Investigating a Hybrid Metaheuristic for Job Shop Rescheduling.- Enhancements to Extremal Optimisation for Generalised Assignment.- Biological Systems II.- Identification of Marker Genes Discriminating the Pathological Stages in Ovarian Carcinoma by Using Support Vector Machine and Systems Biology.- Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
(source: Nielsen Book Data)