Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, Brussels, Belgium, September 4-7, 2006, Proceedings (Lecture Notes in Computer Science (4150))

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. 000......Page 1 Introduction......Page 15 Canonical Particle Swarm Optimizer......Page 16 Fully Informed Particle Swarm Optimizer......Page 17 Experimental Setup......Page 18 Results......Page 19 Conclusions......Page 24 Overview......Page 27 Previous Work......Page 28 Framework Overview......Page 29 Packet Forwarding......Page 30 Metric Update......Page 31 Steady State Routing Probabilities......Page 32 Analysis Overview......Page 34 Effect of Network Sample Rate......Page 35 Conclusion......Page 36 Introduction......Page 39 The Traffic Assignment Problem......Page 40 Theoretical Properties and Classical Solution Algorithms of the Traffic Assignment Problem......Page 42 The Proposed Assignment Algorithm......Page 45 First Results......Page 47 Conclusions and Research Prospects......Page 49 Introduction......Page 51 Metrics for Path Quality and Pheromone Tables......Page 53 Proactive Path Maintenance and Exploration......Page 54 Stochastic Data Routing......Page 55 Experimental Methodology and Characteristics of the Simulation Environment......Page 56 Using Different Optimization Metrics for Pheromone Definition......Page 57 Varying the Number of Entries in Pheromone Diffusion Messages......Page 58 Varying the Routing Exponent for Ants and Data......Page 59 References......Page 61 Introduction......Page 63 Related Work......Page 64 Basic Ant Based Routing for WSNs......Page 66 Improved Ant Based Routing for WSNs......Page 67 Energy-efficient Ant Based Routing for WSNs......Page 68 Experimental Results......Page 69 Conclusions......Page 72 Introduction......Page 74 A Review of Aggregation Pheromone System (APS)......Page 75 The Model of the eAPS......Page 76 Updating the Pheromone Intensity and Sampling New Individuals......Page 77 Experimental Methodology......Page 80 Results......Page 81 Conclusions......Page 84 Introduction......Page 86 Estimation of Distribution Optimization Algorithms......Page 88 Estimation of Distribution Particle Swarm Optimization Algorithm......Page 89 Experimental Setup......Page 91 Results......Page 93 Conclusions......Page 95 Introduction......Page 98 The Knowledge Fusion Problem......Page 99 AntMiner+......Page 100 Decision Tables......Page 102 Using Decision Tables to Validate AntMiner+ Rulesets......Page 103 Incorporating Domain Knowledge in AntMiner+......Page 104 The Use of Heuristics to Incorporate Domain Knowledge......Page 105 Experiments......Page 106 Conclusion......Page 108 Introduction......Page 110 TSALBP-1......Page 112 The Algorithm......Page 113 Computational Results......Page 116 Results for SALBP-1 Instances......Page 117 Results for the TSALBP-1 Instances......Page 119 Conclusions and Outlook to the Future......Page 120 Introduction......Page 122 An Alternative Boundary Search Approach......Page 123 The Boundary Operators......Page 124 The Proposed Method......Page 125 Boundary Approach in ACO Algorithms......Page 126 Analysis of Results......Page 127 Study of the Application of ACO$_{\mathcal B}$......Page 128 Comparison with a State-of-the-Art Algorithm......Page 131 Conclusions and Future Work......Page 132 Introduction......Page 134 The S-bot and Its Simulator......Page 136 Controller......Page 138 Setup......Page 140 Results......Page 141 Conclusions and Future Work......Page 143 Introduction......Page 146 Abstracting PSO States and State Transitions......Page 148 Particle Communication as It Is and as It Could Be......Page 150 Experimental Setup......Page 151 Results......Page 153 Discussion......Page 155 Introduction......Page 158 The Mark-Ant-Walk (MAW) Algorithm......Page 159 Related Work......Page 160 MAW - Formal Proof of Correctness and Upper Time Bound......Page 161 Repetitive Coverage......Page 163 Using Other Metrics......Page 165 Comparing MAW to Other Algorithms......Page 166 Conclusions......Page 168 Introduction......Page 170 Incremental Local Search in Ant Colony Optimization for the Quadratic Assignment Problem......Page 172 Experiments......Page 173 Analysis......Page 175 Conclusions......Page 179 The Individual Discrimination Capabilities......Page 181 Hydrocarbons Profile and Communication in Ants......Page 182 The Foraging Strategy in Social Insects......Page 183 The Mean Field Model......Page 184 Results......Page 186 Discussion......Page 189 Introduction......Page 193 QAP, $\mathcal{MAX--MIN}$ Ant System and Iterated Ants......Page 194 Computational Study......Page 197 Study of ia$\mathcal{MMAS} Parameters......Page 198 Comparison of $\mathcal{MMAS} and ia$\mathcal{MMAS}$......Page 199 Discussion and Conclusions......Page 203 Introduction......Page 205 Methods......Page 207 Results......Page 211 Discussion......Page 213 Introduction......Page 217 $\mathcal{MAX--MIN}$ Ant System......Page 218 Number of Ants $m$......Page 219 Evaporation Rate $\rho$......Page 220 Exponent Values $\alpha$ and $\beta$......Page 221 Experiments......Page 223 Conclusion......Page 226 Introduction......Page 229 Preliminary Definitions......Page 230 Ant System......Page 231 Strongly-Invariant Ant System......Page 235 Conclusions......Page 236 Introduction......Page 238 Parallel Implementation of $\mathcal{MAX--MIN}$ Ant System......Page 239 Experimental Setup......Page 241 Results......Page 242 Conclusions......Page 246 Introduction......Page 249 Cell Definition......Page 250 General Use Placement Constraints......Page 251 Particle Swarm Optimization (PSO)......Page 254 Extend PSO Searching Space......Page 255 Overlap Detection and Removal Mechanism......Page 256 Experiments......Page 257 Conclusions......Page 259 Introduction......Page 261 PLANTS......Page 262 Parameter Optimization and Validation of PLANTS......Page 266 Virtual Screening......Page 269 Conclusions......Page 271 Introduction......Page 273 Overview......Page 274 Algorithm Description......Page 275 Modelling of Perceptional Noise......Page 277 Simulation Experiments......Page 278 Performance of the Algorithm in the Presence of Noise......Page 279 Glowworms......Page 280 Sound Source Localization......Page 281 Conclusions......Page 282 Motivation and Related Work......Page 284 Ant Colony Optimization......Page 285 AntDA: An ACO Algorithm for DA$rep$......Page 287 Transitioning from ${\langle d,q \rangle}$ Pairs to Servers......Page 288 Pheromone Update Rule......Page 290 Experimental Validation of AntDA......Page 291 Conclusion......Page 294 Introduction......Page 296 System Model......Page 297 Cross Entropy Ants (CE-ants)......Page 298 Rate Adaptation Strategies......Page 299 Fixed Rate......Page 300 Implicit Adaptation......Page 301 Case Studies of a National-Wide Internet Topology......Page 302 Concluding Remarks......Page 306 Introduction......Page 308 Pareto Ant Colony Optimization......Page 309 PACO with Path Relinking......Page 311 Evaluation Metrics......Page 313 Analysis......Page 314 Conclusion and Future Direction......Page 316 Introduction......Page 320 Unidirectional Case......Page 321 Bidirectional Case......Page 324 Summary and Discussions......Page 326 Introduction......Page 330 A Pure PSO Algorithm......Page 331 PSO with Local Search......Page 333 Experimental Results......Page 334 Conclusion......Page 336 Introduction......Page 338 Pheromone Update......Page 339 Methods to Avoid Premature Convergence......Page 340 Primary Experiments for Parameter Setting......Page 341 Comparison with Other Algorithms......Page 342 Conclusions......Page 344 Introduction......Page 346 General Ideas......Page 347 Parallel MMAS (PMMAS)......Page 348 Parallel ACS (PACS)......Page 349 General Description......Page 350 Comparison with the Sequential Algorithms......Page 351 Conclusion and Future Work......Page 352 Introduction......Page 354 Ant Algorithms......Page 355 Mathematical Formulation......Page 356 Computing Procedure......Page 357 Numerical Experiments......Page 359 Conclusion......Page 360 Introduction......Page 362 Formulation of the CFCLP......Page 363 Ant Colony Optimization for the CFCLP......Page 364 Computational Experience......Page 367 Conclusions......Page 368 Introduction......Page 370 Problem Definition......Page 371 Ant Colony System for Solving the OVRP......Page 372 Experiment Setting......Page 374 Computational Results and Comparison with Other Algorithms......Page 375 Conclusions......Page 377 Introduction......Page 378 Modified Ant-Based Clustering......Page 379 Parameters Analysis......Page 381 Memory Threshold......Page 382 Experimental Results......Page 383 Conclusion......Page 384 Introduction......Page 386 Orthogonal Experimental Design......Page 387 The Orthogonal Search Embedded ACO Algorithm......Page 388 Grid Selection......Page 389 Elitist Set Construction......Page 390 Computational Results and Discussing......Page 391 Conclusion......Page 392 Introduction......Page 394 The First Aid Problem......Page 395 Ant Colony System Model......Page 396 Reacting to a Change......Page 397 Experiment Environment......Page 398 Tests and Results......Page 399 Conclusion......Page 401 Introduction......Page 402 Gossip-Based Diffusion......Page 403 Artificial Ant for Information Dissemination......Page 404 Ant's Gossiping Activity......Page 405 Simulation Environment and Performance Criteria......Page 406 Results......Page 407 Conclusion and Future Directions......Page 408 Introduction......Page 410 The Proposed Tiled Architecture......Page 411 Computational Tile......Page 412 Agent Intelligence and Colony Behavior......Page 413 Cell Health and Fault Tolerance......Page 414 Synthesis and Experimental Results......Page 415 Performances in Case of Faults......Page 416 Conclusions......Page 417 Introduction......Page 418 Distributed Shortest-Path Algorithm......Page 419 Shortest Path Negotiation Phase......Page 420 Experiments......Page 423 Future Work......Page 424 The Fleet Preventive Maintenance Scheduling Problem - FPMSP......Page 426 Ant System for the FPMSP - $ASmnt$......Page 428 Tests and Applications......Page 429 Conclusions and Recommendations......Page 432 Introduction......Page 434 Inversion for Bottom Geoacoustic Parameters......Page 435 ACO and Other Metaheuristics for Inversion......Page 436 Tuning of $\mathcal{MAX-MIN}$ Ant System......Page 437 Results for Yellow Shark......Page 438 Uncertainty Analysis......Page 439 Conclusion......Page 440 Introduction......Page 442 Pheromone Models......Page 443 Using Higher Order Models......Page 444 Defining $C_c$, $f$ and $\tau_1$ for a Problem......Page 445 Utility of Higher Order Pheromones......Page 447 Conclusions......Page 448 Introduction......Page 450 Hybrid Particle Swarm Optimization......Page 451 Experiments on PSO Variants......Page 452 Conclusions......Page 456 Solution Construction......Page 458 Pheromone Update......Page 459 Pheromone as Probability......Page 460 Function Optimization Problem......Page 461 Multidimensional Knapsack Problem......Page 462 Conclusions......Page 464 ACS for the Minimum Vertex Cover Problem......Page 466 Random Proportional Transition Rule......Page 467 Parameterized Complexity......Page 468 Ant Colony System with Structure......Page 469 Challenging Benchmarks......Page 470 Random Graphs......Page 471 Conclusion......Page 472 Introduction......Page 474 Modified MHRM Heuristic......Page 475 Discrete Particle Swarm Optimization Algorithm......Page 477 Computational Results......Page 478 Conclusions......Page 480 Introduction......Page 482 SVM Classification Error......Page 483 The Proposed ACO Procedure......Page 484 Standard Datasets......Page 486 Electronic Nose Datasets......Page 487 Conclusion......Page 489 Introduction......Page 490 General Description of the Exhibition......Page 491 Module 1: Following Pheromone Trails......Page 492 Module 2: The Foraging Boe-Bot......Page 493 Module 3: Team Work......Page 494 Module 5: Artistic Design......Page 495 Conclusion......Page 496 Introduction......Page 498 Job Shop Scheduling and Solution Construction......Page 499 A Real-World JSP......Page 500 ACO for a Fuzzy JSP......Page 502 Solution Quality......Page 503 Conclusions......Page 504 Introduction......Page 506 General Modifications for the Exam Timetabling Problem......Page 507 Computational Experiments......Page 509 Comparison with Other Exam Timetabling Approaches......Page 510 Conclusion......Page 512 Experimental Result and Concluding Remarks......Page 514 502......Page 516 504......Page 518 Introduction......Page 520 Experiments and Preliminary Results......Page 521 Multiple Ant Colony System......Page 522 Summary......Page 523 Introduction and Problem Formulation......Page 524 Energy Efficient Sink Node Placement Using Particle Swarm Optimization......Page 525 512......Page 526 Approach......Page 528 Conclusion......Page 529 516......Page 530 Simulation Police Allocation and Criminal Activity......Page 532 Evaluation of the Learning Models......Page 533 Approach, Scenario and Results......Page 534 522......Page 536 600......Page 538 If you want to build applications that take full advantage of Windows Vista's new user interface capabilities, you need to learn Microsoft's Windows Presentation Foundation (WPF). This new edition, fully updated for the official release of .NET 3.0, is designed to get you up to speed on this technology quickly. By page 2, you'll be writing a simple WPF application. By the end of Chapter 1, you'll have taken a complete tour of WPF and its major elements. WPF is the new presentation framework for Windows Vista that also works with Windows XP. It's a cornucopia of new technologies, which includes a new graphics engine that supports 3-D graphics, animation, and more; an XML-based markup language, called XAML, for declaring the structure of your Windows UI; and a radical new model for controls. This second edition includes new chapters on printing, XPS, 3-D, navigation, text and documents, along with a new appendix that covers Microsoft's new WPF/E platform for delivering richer UI through standard web browsers -- much like Adobe Flash. Content from the first edition has been significantly expanded and modified. Programming WPF includes: Scores of C# and XAML examples that show you what it takes to get a WPF application up and running, from a simple "Hello, Avalon" program to a tic-tac-toe game Insightful discussions of the powerful new programming styles that WPF brings to Windows development, especially its new model for controls A color insert to better illustrate WPF support for 3-D, color, and other graphics effects A tutorial on XAML, the new HTML-like markup language for declaring Windows UI An explanation and comparison of the features that support interoperability with Windows Forms and other Windows legacy applications WPF represents the best of the control-based Windows world and the content-based web world. Programming WPF helps you bring it all together. ANTS - The International Workshop on Ant Colony Optimization and Swarm Intelligence is now at its?fth edition. The series started in 1998 with the - ganization of ANTS 1998. At that time the goal was to gather in a common meeting those researchers interested in ant colony optimization: more than 50 researchers from around the world joined for the?rst time in Brussels, Belgium, to discuss ant colony optimization and swarm intelligence related research. A selectionofthebest paperspresentedatthe workshopwaspublished asa special issue of the Future Generation Computer Systems journal (Vol. 16, No. 8, 2000). Two years later, ANTS 2000, organized again in Brussels, attracted more than 70 participants. The 41 extended abstracts presented as talks or posters at the workshopwere collected in a booklet distributed to participants, and a selection of the best papers was published as a special section of the IEEE Transactions on Evolutionary Computation (Vol. 6, No. 4, 2002). After these?rst two successful editions, it was decided to make of ANTS a seriesofbiannualeventswitho?cialworkshopproceedings. Thethirdandfourth editions were organized in September 2002 and September 2004, respectively. Proceedings were published by Springer within the Lecture Notes in Computer Science (LNCS) series. The proceedings of ANTS 2002, LNCS Volume 2463, contained 36 contri- tions: 17 full papers, 11 short papers, and 8 extended abstracts, selected out of a total of 52 submissions. Those of ANTS 2004, LNCS Volume 3172, contained 50 contributions:22 full papers, 19 shortpapers, and 9 extended abstracts, selected out of a total of 79 submissions

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.