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  1. Administrator
    AdMiN
    هیات مدیره
    تاریخ عضویت:
    ‏3/10/13
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    Ant colony optimization and swarm intelligence: 5th international workshop
    [​IMG]

    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.

    Table of contents :
    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​
     
    Hossein Ranjbaran از این پست تشکر کرده است.
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