Physicomimetics: physics-based swarm intelligence
Gespeichert in:
| Weitere Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
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| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben Weitere Ausgabe: Online-Ausg.: Ph: Physicomimetics |
| Beschreibung: | XXX, 643 S. Ill., graph. Darst. |
| ISBN: | 9783642228032 3642228038 |
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| 245 | 1 | 0 | |a Physicomimetics |b physics-based swarm intelligence |c William M. Spears ; Diana F. Spears eds. |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2011 | |
| 300 | |a XXX, 643 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
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IMAGE 1
CONTENTS
NETLOGO SYNTAX GLOSSARY XXVII
NETLOGO PARAMETERS GLOSSARY XXIX
PART I INTRODUCTION
1 NATURE IS LAZY 3
WILLIAM M. SPEARS 1.1 WHAT ARE SWARMS? 3
1.2 WHY DO WE CARE ABOUT SWARMS? 4
1.3 WHAT IS THE MODERN HISTORY OF SWARMS? 7
1.4 WHY DO WE NEED PHYSICOMIMETICS? 9
1.5 WHERE DID PHYSICOMIMETICS COME PROM? 13
1.6 WHAT IS THIS BOOK ABOUT? 18
1.6.1 A HOLISTIC VIEW 19
1.6.2 AN ATOMISTIC VIEW 20
1.7 WHERE DO WE GO FROM HERE? 24
2 NETLOGO AND PHYSICS 27
WILLIAM M. SPEARS 2.1 A SPRING IN ONE DIMENSION 27
2.1.1 NETLOGO DETAILS AND NEWTONIAN PHYSICS 32 2.1.2 CONSERVATION LAWS
38
2.2 A SPRING IN TWO DIMENSIONS 42
2.3 A SIMPLE SOLAR SYSTEM 47
3 NETLOGO AND PHYSICOMIMETICS 55
WILLIAM M. SPEARS 3.1 BIOMIMETIC FORMATIONS 55
3.2 PHYSICOMIMETIC FORMATIONS 65
3.2.1 HOOKE'S LAW FOR TRIANGULAR FORMATIONS 67
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1013145941
DIGITALISIERT DURCH
IMAGE 2
XII CONTENTS
3.2.2 NEWTON'S GRAVITATIONAL FORCE LAW FOR TRIANGULAR FORMATIONS 73
3.2.3 NEWTON'S GRAVITATIONAL FORCE LAW FOR SQUARE FORMATIONS 77
3.2.4 NEWTON'S GRAVITATIONAL FORCE LAW FOR HONEYCOMB FORMATIONS 82
3.3 PHYSICOMIMETICS FOR MOVING FORMATIONS 84
3.4 HARDWARE REQUIREMENTS 90
3.5 SUMMARY 92
4 PUSHING THE ENVELOPE 93
WILLIAM M. SPEARS 4.1 PERFECT FORMATIONS 93
4.2 CHAIN FORMATIONS 100
4.3 UNIFORM COVERAGE 107
4.4 COUETTE FLOW 116
4.5 SUMMARY 124
PART II ROBOTIC SWARM APPLICATIONS
5 LOCAL ORIENTED POTENTIAL FIELDS: SELF DEPLOYMENT AND COORDINATION OF
AN ASSEMBLING SWARM OF ROBOTS 129 ANDREA BRAVI, PAOLO CORRADI, FLORIAN
SCHLACHTER, AND ARIANNA MENCIASSI 5.1 INTRODUCTION 129
5.2 METHODS 131
5.2.1 BEHAVIORAL COORDINATION 131
5.2.2 LOCAL ORIENTED POTENTIAL FIELDS 132
5.3 APPLICATIONS 133
5.3.1 LOCAL ORIENTED POTENTIAL FIELDS FOR DEPLOYMENT. 133 5.3.2
ACHIEVING BEHAVIORAL COORDINATION 136
5.4 EXPERIMENTAL RESULTS 137
5.5 DISCUSSION 142
5.5.1 DEPLOYMENT PROPERTIES 142
5.5.2 SENSORIAL AND ACTUATIVE PROPERTIES 143
5.5.3 BEHAVIORAL COORDINATION, PHYSICOMIMETICS, AND LOPFS 143
5.6 CONCLUSIONS 144
6 PHYSICOMIMETICS FOR DISTRIBUTED CONTROL OF MOBILE AQUATIC SENSOR
NETWORKS IN BIOLUMINESCENT ENVIRONMENTS 145 CHRISTER KARLSSON, CHARLES
LEE FREY, DIMITRI V. ZARZHITSKY, DIANA F. SPEARS, AND EDITH A. WIDDER
6.1 INTRODUCTION 145
6.2 BACKGROUND ON BIOLUMINESCENCE 146
6.2.1 THE COASTAL BIOLUMINESCENCE APPLICATION 148
IMAGE 3
CONTENTS XIII
6.2.2 TASKS WITHIN THIS APPLICATION 149
6.3 NETLOGO BIOLUMINESCENCE SIMULATOR 151
6.3.1 SIMULATING DINOFLAGELLATES USING NETLOGO PATCHES. 152 6.3.2
SIMULATING THE AUTONOMOUS SURFACE VEHICLES 157 6.3.3 SEEKING THE MAXIMUM
OR THE MINIMUM 157
6.3.4 SEEKING THE GOAL 160
6.3.5 GRAPHICAL USER INTERFACE AND PARAMETERS 161
6.3.6 THEORY FOR SETTING PARAMETERS 165
6.3.7 EXPERIMENTAL PERFORMANCE ON THE TWO TASKS 167 6.4 MULTI-DRONE
SIMULATOR (MDS) FOR THE BIOLUMINESCENCE APPLICATION 179
6.4.1 MOTIVATION 179
6.4.2 MDS ARCHITECTURE 181
6.4.3 USING THE MDS 187
6.5 HARDWARE DRONE ASV PLATFORMS 187
6.5.1 HARDWARE OBJECTIVES 187
6.5.2 DRONE SPECIFICATIONS 189
6.5.3 TESTING IN FLORIDA'S WATERS 191
6.6 CHAPTER SUMMARY 191
7 GAS-MIMETIC SWARMS FOR SURVEILLANCE AND OBSTACLE AVOIDANCE. 193
WESLEY KERR 7.1 INTRODUCTION 193
7.2 THE COVERAGE TASK 194
7.3 PHYSICS-BASED SWARM SYSTEM DESIGN 195
7.4 KINETIC THEORY 197
7.4.1 ROBOT-WALL COLLISIONS 201
7.4.2 ROBOT-ROBOT COLLISIONS 203
7.5 THEORETICAL PREDICTIONS 205
7.6 EXPERIMENT 1: SPATIAL DISTRIBUTION 208
7.7 EXPERIMENT 2: AVERAGE SPEED 209
7.8 EXPERIMENT 3: VELOCITY DISTRIBUTION 210
7.9 SUMMARY OF THEORETICAL/EMPIRICAL COMPARISONS 213
7.10 PERFORMANCE EVALUATION ALGORITHMS 213
7.10.1 DEFAULT CONTROLLER 213
7.10.2 ANT CONTROLLER 214
7.11 PERFORMANCE EVALUATION: EXPERIMENTAL SETUP 215
7.12 PERFORMANCE EVALUATION: EXPERIMENTAL RESULTS 216
7.12.1 TEMPORAL COVERAGE (W) RESULTS 216
7.12.2 TOTAL SPATIAL COVERAGE (P C ) RESULTS 218
7.12.3 SHADOW COVERAGE (P S ) RESULTS 219
7.13 PERFORMANCE EVALUATION: CONCLUSIONS 220
7.14 SUMMARY AND FUTURE WORK 221
IMAGE 4
XIV CONTENTS
8 A MULTI-ROBOT CHEMICAL SOURCE LOCALIZATION STRATEGY BASED ON FLUID
PHYSICS: THEORETICAL PRINCIPLES 223
DIANA F. SPEARS, DAVID R. THAYER, AND DIMITRI V. ZARZHITSKY 8.1
INTRODUCTION 223
8.2 BACKGROUND 225
8.2.1 BRIEF OVERVIEW OF THE CHEMICAL SOURCE LOCALIZATION TASK 225
8.2.2 FLUID DYNAMICS AS A PHYSICOMIMETIC FOUNDATION FOR SOLVING THE TASK
226
8.2.3 THE CHEMICAL SOURCE LOCALIZATION TASK AND ITS SUBTASKS 229
8.2.4 APPLYING SWARMS OF ROBOTS TO THE TASK 231
8.3 PRIOR RESEARCH ON BIOMIMETIC CHEMICAL PLUME TRACING . . . 233 8.3.1
CHEMOTAXIS 234
8.3.2 ANEMOTAXIS 234
8.3.3 HYBRID STRATEGIES 235
8.4 PHYSICOMIMETIC CHEMICAL SOURCE LOCALIZATION ALGORITHM . . 235 8.4.1
EMITTER IDENTIFICATION BASED ON FLUID DYNAMICS . . 236 8.4.2 DERIVING
OUR FLUXOTAXIS SWARM NAVIGATION STRATEGY FROM THE EMITTER SIGNATURE 238
8.5 THEORETICAL COMPARISONS OF BIOMIMETIC AND PHYSICOMIMETIC APPROACHES
TO THE TASK 240
8.5.1 ANEMOTAXIS VERSUS FLUXOTAXIS 241
8.5.2 CHEMOTAXIS VERSUS FLUXOTAXIS 242
8.5.3 THE CONTINUOUS EMITTER CASE 244
8.5.4 THE SINGLE-PUFF EMITTER CASE 246
8.6 SUMMARY AND CONCLUSIONS 249
9 A MULTI-ROBOT CHEMICAL SOURCE LOCALIZATION STRATEGY BASED ON FLUID
PHYSICS: EXPERIMENTAL RESULTS 251
DIMITRI V. ZARZHITSKY 9.1 INTRODUCTION 251
9.2 MOTIVATION AND BACKGROUND 253
9.2.1 CHEMICAL PLUME 255
9.2.2 CPT ALGORITHMS 257
9.2.3 FLUXOTAXIS FOR CPT 258
9.3 NUMERICAL MODEL OF THE CHEMICAL PLUME 260
9.3.1 ADVECTION DUE TO WIND 263
9.3.2 MIXING DUE TO RANDOM VELOCITY 263
9.3.3 GROWTH DUE TO DIFFUSION 263
9.3.4 DENSITY COMPUTATION 264
9.4 SWARMS OF CHEMICAL PLUME-TRACING ROBOTS 264
9.4.1 PHYSICOMIMETICS CONTROL ARCHITECTURE 265 9.4.2 SIMULATED
ENVIRONMENT 266
9.4.3 GENERAL IMPLEMENTATION DETAILS 267
IMAGE 5
CONTENTS XV
9.4.4 SEVEN-ROBOT LATTICE IMPLEMENTATION 269
9.4.5 LARGE SWARM IMPLEMENTATION 275
9.5 SEVEN-ROBOT CPT STUDY 281
9.5.1 EXPERIMENT IN AN UNOBSTRUCTED ENVIRONMENT 281 9.5.2 EXPERIMENT IN
AN OBSTRUCTED ENVIRONMENT 284 9.6 CPT STUDY OF A LARGE DECENTRALIZED
SWARM 287
9.6.1 EXPERIMENT WITH INCREASING NUMBER OF OBSTACLES . 287 9.6.2
EXPERIMENT WITH INCREASING SWARM SIZE 292 9.7 LESSONS LEARNED 295
PART III PHYSICOMIMETICS ON HARDWARE ROBOTS
10 WHAT IS A MAXELBOT? 301
PAUL M. MAXIM 10.1 INTRODUCTION 301
10.2 LOCALIZATION 301
10.3 TRIANGULATION VERSUS TRILATERATION 302
10.4 OUR TRILATERATION APPROACH 303
10.5 TRILATERATION IMPLEMENTATION 305
10.5.1 MEASURING DISTANCE 305
10.5.2 SRF04 ULTRASONIC RANGE FINDER 305
10.5.3 OUR PROTOTYPE XSRF 307
10.5.4 TRILATERATION MODULE 314
10.6 TRILATERATION EVALUATION 319
10.6.1 TRILATERATION ACCURACY EXPERIMENT 320
10.7 SYNCHRONIZATION PROTOCOL 320
10.7.1 HIGH-LEVEL PROTOCOL DESCRIPTION 321
10.7.2 LOW-LEVEL PROTOCOL DESCRIPTION 322
10.7.3 PROTOCOL RECOVERY 324
10.8 MAXELBOT 325
10.8.1 CENTRALIZED VERSUS MODULARIZED 325
10.8.2 INTER-INTEGRATED CIRCUIT I 2 C BUS 326
10.8.3 TRILATERATION MODULE CONNECTIVITY 327
10.8.4 MOBILE ROBOT PLATFORM 330
10.8.5 PIC-BASED CONTROLLER BOARD 331
10.9 ADD-ONS 333
10.9.1 OBSTACLE DETECTION MODULE 333
10.9.2 LCD 335
10.9.3 DIGITAL COMPASS 336
10.9.4 DIGITAL TEMPERATURE SENSOR 336
10.9.5 SERIAL OVER BLUETOOTH 337
10.10 SUMMARY 337
IMAGE 6
XVI CONTENTS
11 UNIFORM COVERAGE 341
PAUL M. MAXIM 11.1 INTRODUCTION 341
11.2 SIMULATION 342
11.2.1 ENVIRONMENT SETUP 342
11.2.2 CONFIGURATION AND CONTROL 343
11.2.3 KULLBACK-LEIBLER DIVERGENCE 344
11.2.4 MARKOV CHAIN ANALYSIS 345
11.2.5 FIRST ALGORITHM 347
11.2.6 ENVIRONMENTS 347
11.2.7 SECOND ALGORITHM 348
11.3 DERIVATION OF THE MEAN FREE PATH 349
11.4 FURTHER CONFIRMATION 352
11.5 THEORY FOR MULTIPLE ROBOTS 353
11.6 HARDWARE IMPLEMENTATION 353
11.6.1 SQUARE ENVIRONMENT 356
11.6.2 RECTANGULAR ENVIRONMENT 359
11.6.3 L-SHAPED ENVIRONMENT 363
11.7 SUMMARY 366
12 CHAIN FORMATIONS 367
PAUL M. MAXIM 12.1 INTRODUCTION 367
12.2 MODIFICATION TO THE FORCE LAW 368
12.3 BACKFLOW FORCE 370
12.4 FRONT TANGENTIAL FORCE 371
12.5 PARAMETER OPTIMIZATION WITH EVOLUTIONARY ALGORITHMS 371 12.6
SIMULATION 372
12.6.1 A MAXELBOT-FAITHFUL SIMULATION ENGINE 373 12.6.2 PERFORMANCE
METRICS 379
12.6.3 EXPERIMENTAL TEST ENVIRONMENTS 381
12.6.4 EXPERIMENTAL DESIGN 381
12.6.5 EXPERIMENT I: VARIABLE NUMBER OF ROBOTS 383 12.6.6 EXPERIMENT II:
EFFECT OF MOTOR AND SENSOR NOISE . . 384 12.6.7 EXPERIMENT III:
ROBUSTNESS 386
12.6.8 BACKFLOW FORCE ANALYSIS 388
12.6.9 ANALYSIS OF AN IDEAL CHAIN FORMATION 388
12.7 HARDWARE IMPLEMENTATION 390
12.7.1 MODIFIED COMMUNICATION PROTOCOL 391
12.7.2 REAL-TIME GRAPHICAL REPRESENTATION TOOL 395 12.7.3 CONNECTIVITY
OPTIONS 396
12.7.4 COMMUNICATION PROTOCOL PERFORMANCE ANALYSIS 397 12.7.5 STATIC
MAXELBOT EXPERIMENTS 398
12.7.6 DYNAMIC MAXELBOT EXPERIMENTS 404
12.8 SUMMARY AND CONCLUSIONS 412
IMAGE 7
CONTENTS XVII
13 PHYSICOMIMETIC MOTION CONTROL OF PHYSICALLY CONSTRAINED AGENTS. 413
THOMAS B. APKER AND MITCHELL A. POTTER 13.1 INTRODUCTION 413
13.1.1 CHALLENGES FOR PHYSICAL IMPLEMENTATION 414 13.1.2 DEFINING
PHYSICALLY USEFUL ARTIFICIAL PHYSICS 414 13.2 THE EXTENDED BODY CONTROL
PROBLEM 415
13.2.1 REACHABLE SPACE FOR REAL VEHICLES 418
13.2.2 CASE STUDY: WHEELED ROBOTS 422
13.2.3 CASE STUDY: FIXED-WING UAV 426
13.2.4 SUMMARY 429
13.3 BEYOND POINT-MASS PARTICLES 429
13.3.1 INCORPORATING VEHICLE HEADING 429
13.4 MULTI-PARTICLE AGENTS 431
13.4.1 CASE STUDY: WHEELED ROBOT 432
13.4.2 CASE STUDY: FIXED-WING UAV 434
13.5 CONCLUSIONS 435
PART IV PREDICTION, ADAPTATION, AND SWARM ENGINEERING
14 ADAPTIVE LEARNING BY ROBOT SWARMS IN UNFAMILIAR ENVIRONMENTS. 441
SURANGA HETTIARACHCHI 14.1 INTRODUCTION 441
14.2 ADAPTIVE SWARMS 442
14.3 LENNARD-JONES FORCE LAW 443
14.4 SWARM LEARNING: OFFLINE 444
14.5 THE DAEDALUS PARADIGM 453
14.6 SWARM LEARNING: ONLINE 454
14.7 ROBOT LEARNING WITH OBSTRUCTED PERCEPTION 458
14.8 SURVIVAL THROUGH COOPERATION 460
14.8.1 INITIAL EXPERIMENTS 460
14.8.2 SURVIVAL IN HIGH OBSTACLE DENSITY ENVIRONMENTS . . 466 14.9
SUMMARY OF RESEARCH 470
14.10 RELATED WORK 470
14.11 FUTURE WORK 472
15 A STATISTICAL FRAMEWORK FOR ESTIMATING THE SUCCESS RATE OF
LIQUID-MIMETIC SWARMS 475
DIANA F. SPEARS, RICHARD ANDERSON-SPRECHER, ALEKSEY KLETSOV, AND ANTONS
REBGUNS 15.1 INTRODUCTION 475
15.1.1 THE MAIN IDEA AND RESEARCH GOALS 476
15.1.2 SUCCESS PROBABILITY AND RISK ASSESSMENT 476 15.1.3 SCOUTS FOR
ASSESSING RISK 477
15.1.4 PHYSICOMIMETICS AS THE CONTROL ALGORITHM 477 15.1.5 CASE STUDY:
NAVIGATION THROUGH OBSTACLES 478 15.1.6 CHAPTER OUTLINE 479
IMAGE 8
XVIII CONTENTS
15.2 A BRIEF REVIEW OF PROBABILITY 479
15.2.1 PROBABILITY AND UNCERTAINTY 480
15.2.2 BERNOULLI TRIALS AND THE BINOMIAL PROBABILITY DISTRIBUTION 480
15.2.3 BAYES' THEOREM FOR CALCULATING THE POSTERIOR PROBABILITY 482
15.3 STATISTICAL SCOUTS FRAMEWORK FOR RISK ASSESSMENT 483 15.3.1
STOCHASTIC PREDICTIONS OF SUCCESS: FROM SCOUTS TO SWARMS 483
15.3.2 THE STANDARD BERNOULLI TRIALS FORMULA 484 15.3.3 OUR NOVEL
BAYESIAN FORMULA 484
15.4 SWARM NAVIGATION SIMULATOR 486
15.5 EXPERIMENTAL DESIGN 488
15.5.1 THE ENVIRONMENTS AND THE PRIOR DISTRIBUTION FOR THE BAYESIAN
FORMULA 489
15.5.2 EXPERIMENTAL PARAMETERS 490
15.5.3 THE PERFORMANCE METRIC 491
15.6 THE EXPERIMENTAL ALGORITHMS 492
15.7 EXPERIMENTAL HYPOTHESES AND RESULTS 493
15.8 EXPERIMENTAL CONCLUSIONS 500
15.9 THE ROLE OF PHYSICOMIMETICS 501
15.10 ISSUES FOR PORTING TO REAL ROBOTS 502
15.11 SUMMARY 502
16 PHYSICOMIMETIC SWARM DESIGN CONSIDERATIONS: MODULARITY, SCALABILITY,
HETEROGENEITY, AND THE PREDICTION VERSUS CONTROL DILEMMA 505
R. PAUL WIEGAND AND CHRIS ELLIS 16.1 INTRODUCTION 505
16.2 EXISTING SWARM ENGINEERING APPROACHES 507
16.3 HETEROGENEOUS BEHAVIORS 508
16.3.1 PHYSICOMIMETICS 509
16.3.2 DIFFERENTIATING PARTICLE TYPES 510
16.3.3 VIRTUAL PARTICLES 511
16.3.4 DIFFERENTIATING INTERACTION TYPES 511
16.4 HETEROGENEOUS PROBLEMS 512
16.4.1 EXAMPLE: CENTRALIZED RESOURCE PROTECTION 512 16.4.2 EXAMPLE:
MULTI-FIELD SURVEILLANCE 514
16.4.3 EXAMPLE: COVERT TRACKING 515
16.5 DESIGNING MODULAR AND SCALABLE HETEROGENEOUS SWARMS . . . 516
16.5.1 GRAPH-BASED INTERACTION DESIGN 516
16.5.2 DESIGN MODULARITY VIA INDEX VARIABLES 517 16.5.3 BEHAVIOR
MODULARITY VIA WILDCARD INDICES 518 16.5.4 DESIGNING FOR SCALABILITY
WITH INTERACTION MODELS . 519 16.5.5 EXAMPLE INTERACTION MODEL 519
IMAGE 9
CONTENTS XIX
16.5.6 UNIT TESTING THE BEHAVIOR MODULES 522
16.6 THE PREDICTION VERSUS CONTROL DILEMMA 523
16.6.1 MODELING NATURAL PHENOMENA 523
16.6.2 CONTROL INPUT 524
16.6.3 SUMMARIZING: THREE SOURCES OF CHALLENGES 525 16.6.4 TESTING
STRATEGIES FOR AGENTS WITH CONSTRAINED MOTION 525
16.7 CONCLUSIONS 526
17 USING SWARM ENGINEERING TO DESIGN PHYSICOMIMETIC SWARMS 529 SANZA
KAZADI 17.1 INTRODUCTION 529
17.2 SWARM ENGINEERING 531
17.3 SWARM ENGINEERING ARTIFICIAL PHYSICS SYSTEMS 535
17.3.1 MICROTHEORY 536
17.3.2 LOWEST ENERGY CONFIGURATION 540
17.3.3 TRANSITIONING BETWEEN MINIMA 541
17.4 SIMULATION 541
17.4.1 DESCRIPTION OF THE SIMULATION 542
17.4.2 SWARM CONTROL METHOD 1 544
17.4.3 SWARM CONTROL METHOD 2 547
17.5 THE QUARK MODEL 548
17.5.1 SIMPLE LOSSLESS FLOCKS 550
17.5.2 DIRECTIONAL COHERENT FLOCKING 552
17.5.3 DIRECTIONAL COHERENT FLOCKING IN THE PRESENCE OF OBSTACLES 554
17.5.4 QUARK MODEL 555
17.6 DISCUSSION 559
17.7 CONCLUSION 560
PART V FUNCTION OPTIMIZATION
18 ARTIFICIAL PHYSICS OPTIMIZATION ALGORITHM FOR GLOBAL OPTIMIZATION 565
LIPING XIE, YING TAN, AND JIANCHAO ZENG 18.1 INTRODUCTION 565
18.2 MOTIVATION 567
18.3 GENERAL FRAMEWORK OF THE APO ALGORITHM 570
18.3.1 APO FRAMEWORK 570
18.3.2 FORCE LAW DESIGN 572
18.3.3 MASS FUNCTION SELECTION 579
18.4 CONVERGENCE ANALYSIS OF THE APO ALGORITHM 581
18.4.1 CONVERGENCE ANALYSIS 582
18.4.2 GUIDELINES FOR SELECTING G 584
18.5 IMPROVEMENTS TO APO 585
18.5.1 AN EXTENDED APO 585
18.5.2 A VECTOR MODEL OF APO 587
IMAGE 10
XX CONTENTS
18.5.3 LOCAL APO 589
18.6 CONCLUSIONS AND FUTURE WORK 589
19 ARTIFICIAL PHYSICS FOR NOISY NONSTATIONARY ENVIRONMENTS 591 WILLIAM
M. SPEARS 19.1 ARTIFICIAL PHYSICS OPTIMIZATION AND TRACKING 591 19.2
NETLOGO IMPLEMENTATION OF APO 593
19.3 COMPARISON OF APO AND PSO 600
19.3.1 COMPARISON OF APO AND PSO IN TWO DIMENSIONS. 601 19.3.2
COMPARISON OF APO AND PSO IN HIGHER DIMENSIONS 606 19.4 SUMMARY 612
A ANOMALOUS BEHAVIOR OF THE RANDOM ( ) NUMBER GENERATOR 615
WILLIAM M. SPEARS AND DEREK T. GREEN
INDEX 623
REFERENCES 629 |
| any_adam_object | 1 |
| author2 | Spears, William M. |
| author2_role | edt |
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| author_facet | Spears, William M. |
| building | Verbundindex |
| bvnumber | BV039939847 |
| classification_rvk | ST 300 |
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| ctrlnum | (OCoLC)778140327 (DE-599)DNB1013145941 |
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| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.89263 |
| dewey-search | 629.89263 |
| dewey-sort | 3629.89263 |
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| discipline | Technik Informatik Elektrotechnik / Elektronik / Nachrichtentechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV039939847 |
| illustrated | Illustrated |
| indexdate | 2024-07-21T00:26:50Z |
| institution | BVB |
| isbn | 9783642228032 3642228038 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024797947 |
| oclc_num | 778140327 |
| open_access_boolean | |
| owner | DE-11 DE-91 DE-BY-TUM |
| owner_facet | DE-11 DE-91 DE-BY-TUM |
| physical | XXX, 643 S. Ill., graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| spelling | Physicomimetics physics-based swarm intelligence William M. Spears ; Diana F. Spears eds. Berlin [u.a.] Springer 2011 XXX, 643 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturangaben Weitere Ausgabe: Online-Ausg.: Ph: Physicomimetics Physical Computing (DE-588)7692229-7 gnd rswk-swf Robotik (DE-588)4261462-4 gnd rswk-swf Bionik (DE-588)4006888-2 gnd rswk-swf Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Schwarmintelligenz (DE-588)4793676-9 s Robotik (DE-588)4261462-4 s Bionik (DE-588)4006888-2 s Physical Computing (DE-588)7692229-7 s DE-604 Spears, William M. edt text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3847289&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024797947&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Physicomimetics physics-based swarm intelligence Physical Computing (DE-588)7692229-7 gnd Robotik (DE-588)4261462-4 gnd Bionik (DE-588)4006888-2 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| subject_GND | (DE-588)7692229-7 (DE-588)4261462-4 (DE-588)4006888-2 (DE-588)4793676-9 (DE-588)4143413-4 |
| title | Physicomimetics physics-based swarm intelligence |
| title_auth | Physicomimetics physics-based swarm intelligence |
| title_exact_search | Physicomimetics physics-based swarm intelligence |
| title_full | Physicomimetics physics-based swarm intelligence William M. Spears ; Diana F. Spears eds. |
| title_fullStr | Physicomimetics physics-based swarm intelligence William M. Spears ; Diana F. Spears eds. |
| title_full_unstemmed | Physicomimetics physics-based swarm intelligence William M. Spears ; Diana F. Spears eds. |
| title_short | Physicomimetics |
| title_sort | physicomimetics physics based swarm intelligence |
| title_sub | physics-based swarm intelligence |
| topic | Physical Computing (DE-588)7692229-7 gnd Robotik (DE-588)4261462-4 gnd Bionik (DE-588)4006888-2 gnd Schwarmintelligenz (DE-588)4793676-9 gnd |
| topic_facet | Physical Computing Robotik Bionik Schwarmintelligenz Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3847289&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024797947&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT spearswilliamm physicomimeticsphysicsbasedswarmintelligence |