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This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions. The papers are devoted to theoretical and foundational aspects of ant algorithms, evolutionary optimization, ant colony optimization, and swarm intelligence and deal with a broad variety of optimization applications in networking, operations research, multiagent systems, robot systems, networking, etc.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, held in Brussels, Belgium in September 2004. The 22 revised full papers, 19 revised short papers, and 9 poster abstracts presented were carefully reviewed and selected from 79 papers submitted. The papers are devoted to theoretical and foundational aspects of ant algorithms, ant colony optimization and swarm intelligence and deal with a broad variety of optimization applications in networking and operations research.
(source: Nielsen Book Data)

• A Graph-Based Developmental Swarm Representation and Algorithm
• A Graph-Based Developmental Swarm Representation and Algorithm
• A Modified Particle Swarm Optimization Algorithm for the Best Low Multilinear Rank Approximation of Higher-Order Tensors
• A Robotic Validation of the Attractive Field Model: An Inter-disciplinary Model of Self-regulatory Social Systems
• A Thermodynamic Approach to the Analysis of Multi-robot Cooperative Localization under Independent Errors
• An Alternative ACO Algorithm for Continuous Optimization Problems
• An Efficient Optimization Method for Revealing Local Optima of Projection Pursuit Indices
• Ant Colony Optimisation for Ligand Docking
• Antbots: A Feasible Visual Emulation of Pheromone Trails for Swarm Robots
• Automatic Configuration of Multi-Objective ACO Algorithms
• Autonomous Morphogenesis in Self-assembling Robots Using IR-Based Sensing and Local Communications
• Autonomous Multi-agent Cycle Based Patrolling
• Biologically Realistic Primitives for Engineered Morphogenesis
• Evaluating the Robustness of Activator-Inhibitor Models for Cluster Head Computation
• Evolution of Self-organised Path Formation in a Swarm of Robots
• Extensions to the Ant-Miner Classification Rule Discovery Algorithm
• Functional Blueprints: An Approach to Modularity in Grown Systems
• Heterogeneous Particle Swarm Optimization
• Modern Continuous Optimization Algorithms for Tuning Real and Integer Algorithm Parameters
• Multi-agent Deployment on a Ring Graph
• Multi-Swarm Optimization for Dynamic Combinatorial Problems: A Case Study on Dynamic Vehicle Routing Problem
• Off-line vs. On-line Tuning: A Study on Ant System for the TSP
• Opinion Dynamics for Decentralized Decision-Making in a Robot Swarm
• Positional Communication and Private Information in Honeybee Foraging Models
• Rank Based Particle Swarm Optimization
• Self-organized Task Partitioning in a Swarm of Robots
• Slime Mold Inspired Path Formation Protocol for Wireless Sensor Networks
• Solving the Multi-dimensional Multi-choice Knapsack Problem with the Help of Ants
• Theoretical Properties of Two ACO Approaches for the Traveling Salesman Problem
• Short Papers
• A Cooperative Network Game Efficiently Solved via an Ant Colony Optimization Approach
• A Deterministic Metaheuristic Approach Using "Logistic Ants" for Combinatorial Optimization
• A Model Based Ant Colony Design for the Protein Engineering Problem
• ACOPHY: A Simple and General Ant Colony Optimization Approach for Phylogenetic Tree Reconstruction
• ACS Searching for D 4t -Hadamard Matrices
• Ant Based Semi-supervised Classification
• Automatic Generation of Optimised Working Time Models in Personnel Planning
• Bee-Sensor: A Step Towards Meta-Routing Strategies in Hybrid Ad Hoc Networks
• Cooperation in a Heterogeneous Robot Swarm through Spatially Targeted Communication
• Early-Stage Diagnosis of Endogenous Diseases by Swarms of Nanobots: An Applicative Scenario
• EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization
• Emergent Flocking with Low-End Swarm Robots
• Exploiting Loose Horizontal Coupling in Evolutionary Swarm Robotics
• Formal Verification of Probabilistic Swarm Behaviours
• Inverse Modeling in Geoenvironmental Engineering Using a Novel Particle Swarm Optimization Algorithm
• Mobile Stigmergic Markers for Navigation in a Heterogeneous Robotic Swarm
• Motif Finding Using Ant Colony Optimization
• Multiple Ant Colony System for Substructure Discovery
• Opportunistic Ant-Based Path Management for Wireless Mesh Networks
• Parallel Ant Colony Optimization Algorithm on a Multi-core Processor
• Particle Swarm Optimization in High Dimensional Spaces
• Particle Swarm Optimization of Bollinger Bands
• Protein Structure Prediction in Lattice Models with Particle Swarm Optimization
• Short and Robust Communication Paths in Dynamic Wireless Networks
• The ACO Encoding
• The Complexity of Grid Coverage by Swarm Robotics
• The Design of an Active Structural Vibration Reduction System Using a Modified Particle Swarm Optimization
• Extended Abstracts
• Ant Colony Extended: Search in Solution Spaces with a Countably Infinite Number of Solutions
• Automatic Parameter Configuration of Particle Swarm Optimization by Classification of Function Features
• Constructing Low-Cost Swarm Robots That March in Column Formation
• Coordinating Heterogeneous Swarms through Minimal Communication among Homogeneous Sub-swarms
• Effect of Particle Initialization on the Performance of Particle Swarm Niching Algorithms
• Energy Efficient Swarm Deployment for Search in Unknown Environments
• Genetic Encoding of Robot Metamorphosis: How to Evolve a Glider with a Genetic Regulatory Network
• How Ant Systems Can Help in Management of pH for Industrial Wastewater Discharges
• Hybrid Metaheuristic Combining Ant Colony Optimization and H-Method
• Increasing Individual Density Reduces Extra-Variance in Swarm Intelligence
• "Look out!": Socially-Mediated Obstacle Avoidance in Collective Transport
• On Possible Connections between Ant Algorithms and Random Matrix Theory
• Soft Variable Fixing in Path Relinking: An Application to ACO Codes
• Training Randomly Connected, Recurrent Artificial Neural Networks Using PSO.

Annotation This book constitutes the proceedings of the 7th International Conference on Swarm Intelligence, held in Brussels, Belgium, in September 2010.

• 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.
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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.
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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.
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• Theoretical Framework.- Evolutionary Computing Techniques in Data Mining.- Ant Colony Decision Tree Approach.- Adaptive Goal Function of the ACDT Algorithm.- Examples of Practical Application.
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This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.
(source: Nielsen Book Data)

• Foreword xi Acknowledgements xiii
• Chapter 1. Introduction 1 1.1. Overview of the book 2
• Chapter 2. Computational Complexity 7 2.1. Complexity of an algorithm 8 2.2. Complexity of a problem 10 2.3. Where the most difficult instances can be found 15 2.4. Solving NP-hard problems in practice 21 PART I. CONSTRAINT PROGRAMMING 27 Introduction to Part I 29
• Chapter 3. Constraint Satisfaction Problems 31 3.1. What is a constraint? 31 3.2. What is a constraint satisfaction problem? 33 3.3. Optimization problems related to CSPs 35 3.4. The n-queens problem 37 3.5. The stable marriage problem 43 3.6. Randomly generated binary CSPs 46 3.7. The car sequencing problem 47 3.8. Discussion 50
• Chapter 4. Exact Approaches 53 4.1. Construction of a search tree 53 4.2. Constraint propagation 57 4.3. Ordering heuristics 60 4.4. From satisfaction to optimization problems 63 4.5. Discussion 65
• Chapter 5. Perturbative Heuristic Approaches 69 5.1. Genetic algorithms 70 5.2. Local search 73 5.3. Particle swarm optimization 78 5.4. Discussion 80
• Chapter 6. Constructive Heuristic Approaches 85 6.1. Greedy randomized approaches 86 6.2. Estimation of distribution algorithms 88 6.3. Ant colony optimization 90 6.4. Discussion 91
• Chapter 7. Constraint Programming Languages 93 7.1. Constraint logic programming 94 7.2. Constraint programming libraries 96 7.3. Constraint-based local search 96 7.4. Discussion 99 PART II. ANT COLONY OPTIMIZATION 101 Introduction to Part II 103
• Chapter 8. From Swarm Intelligence to Ant Colony Optimization 105 8.1. Complex systems and swarm intelligence 106 8.2. Searching for shortest paths by ant colonies 108 8.3. Ant system and the traveling salesman problem 111 8.4. Generic ACO framework 116
• Chapter 9. Intensification versus Diversification 125 9.1. ACO mechanisms for intensifying the search 125 9.2. ACO mechanisms for diversifying the search 127 9.3. Balancing intensification and diversification 128 9.4. Measures of diversification/intensification 135
• Chapter 10. Beyond Static Combinatorial Problems 141 10.1. Multi-objective problems 141 10.2. Dynamic optimization problems 145 10.3. Optimization problems over continuous domains 147
• Chapter 11. Implementation Issues 151 11.1. Data structures 151 11.2. Selection of a component with respect to probabilities 154 11.3. Implementation of a local search procedure 157 11.4. Computation of diversification/intensification measures 157 PART III. CP WITH ACO 161 Introduction to Part III 163
• Chapter 12. Sequencing Cars with ACO 165 12.1. Notation 165 12.2. A first pheromone structure for identifying good car sequences 166 12.3. A second pheromone structure for identifying critical cars 171 12.4. Combining the two pheromone structures 173 12.5. Comparison of the different ACO algorithms 174 12.6. Comparison of ACO with state-of-the-art approaches 178 12.7. Discussion 182
• Chapter 13. Subset Selection with ACO 185 13.1. Subset selection problems 186 13.2. Description of Ant-SSP 189 13.3. Instantiations of Ant-SSP with respect to two pheromone strategies 192 13.4. Instantiation of Ant-SSP to solve CSPs 196 13.5. Experimental results 197 13.6. Discussion 202
• Chapter 14. Integration of ACO in a CP Language 205 14.1. Framework for integrating ACO within a CP library 206 14.2. Illustration of ACO-CP on the car sequencing problem 210 14.3. Discussion 214
• Chapter 15. Conclusion 215 15.1. Towards constraint-based ACO search 215 15.2. Towards a reactive ACO search 216 Bibliography 219 Index 231.
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Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. The second part describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part shows how the ant colony may be integrated within a constraint programming language, thus combining the expressive power of constraint programming languages, to describe problems in a declarative way, and the solving power of ant colony optimization to efficiently solve these problems.
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• Introduction.- State of the Art.- Problem Formulation and Optimization Approach.- MTS Algorithms for Cardinal UAV Motion Models.
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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)

• Artificial Neural Networks
• Genetic Algorithm
• Ant Colony Optimization (ACO)
• An Application for Process System Control
• Conclusion.

Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

• Evolutionary and DNA computing
• Intelligent and complex systems
• Ant colony optimization
• Simulation and modeling : applications in medical sciences.

This book contains selected papers presented at the International Conference "Bio-Inspired Computational Methods Used for Difficult Problems Solving. Development of Intelligent and Complex Systems" - BICS 2008, organized by the Romanian Academy, Bucharest and Petru Major University of Tg. Mures, held on 5-7 November 2008 at Petry Major University of Tg. Mures, Romania. The aim of this conference was to bring together researchers working in the main areas of Complex Systems, Artificial Intelligence, Intelligent Systems and Natural Computing for presenting their recent results and exchanging ideas. The papers included in the book provide an introduction and also a state of the art to the bio-inspired computer science. The areas covered were those of natural computing, such as evolutionary and neural computing, as well as younger research directions, such as DNA computing, ant colony optimization, with connections and applications to intelligent complex systems and to medical informatics. Thus, both directions of research, from biology to informatics and from informatics to biology are illustrated.
(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.
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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.
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This report records the work and contributions of the NITRD-funded Bio-Inspired Approaches to Moving-Target Defense Strategies project performed by Pacific Northwest National Laboratory under the technical guidance of the National Security Agency’s R6 division. The project has incorporated a number of bio-inspired cyber defensive technologies within an elastic framework provided by the Digital Ants. This project has created the first scalable, real-world prototype of the Digital Ants Framework (DAF)[11] and integrated five technologies into this flexible, decentralized framework: (1) Ant-Based Cyber Defense (ABCD), (2) Behavioral Indicators, (3) Bioinformatic Clas- sification, (4) Moving-Target Reconfiguration, and (5) Ambient Collaboration. The DAF can be used operationally to decentralize many such data intensive applications that normally rely on collection of large amounts of data in a central repository. In this work, we have shown how these component applications may be decentralized and may perform analysis at the edge. Operationally, this will enable analytics to scale far beyond current limitations while not suffering from the bandwidth or computational limitations of centralized analysis. This effort has advanced the R6 Cyber Security research program to secure digital infrastructures by developing a dynamic means to adaptively defend complex cyber systems. We hope that this work will benefit both our client’s efforts in system behavior modeling and cyber security to the overall benefit of the nation.