Swarm Intelligence

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  • Format: Hardcover
  • Copyright: 2001-03-26
  • Publisher: Elsevier Science
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Traditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm Intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodologyparticle swarmswhich focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method. This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional. * Places particle swarms within the larger context of intelligent adaptive behavior and evolutionary computation. * Describes recent results of experiments with the particle swarm optimization (PSO) algorithm * Includes a basic overview of statistics to ensure readers can properly analyze the results of their own experiments using the algorithm. * Support software which can be downloaded from the publishers website, includes a Java PSO applet, C and Visual Basic source code.

Author Biography

James Kennedy is a social psychologist who works in survey methods at the U.S. Department of Labor Russell C. Eberhart is Associate Dean for Research, Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis Yuhui Shi received his Ph.D. in electrical engineering from Southeast University, China, in 1992 and is an applied specialist for Electronic Data Systems, Inc.

Table of Contents

Prefacep. xiii
Models and Concepts of Life and Intelligencep. 3
The Mechanics of Life and Thoughtp. 4
Stochastic Adaptation: Is Anything Ever Really Random?p. 9
The "Two Great Stochastic Systems"p. 12
The Game of Life: Emergence in Complex Systemsp. 16
The Game of Lifep. 17
Emergencep. 18
Cellular Automata and the Edge of Chaosp. 20
Artificial Life in Computer Programsp. 26
Intelligence: Good Minds in People and Machinesp. 30
Intelligence in People: The Boring Criterionp. 30
Intelligence in Machines: The Turing Criterionp. 32
Symbols, Connections, and Optimization by Trial and Errorp. 35
Symbols in Trees and Networksp. 36
Problem Solving and Optimizationp. 48
A Super-Simple Optimization Problemp. 49
Three Spaces of Optimizationp. 51
Fitness Landscapesp. 52
High-Dimensional Cognitive Space and Word Meaningsp. 55
Two Factors of Complexity: NK Landscapesp. 60
Combinatorial Optimizationp. 64
Binary Optimizationp. 67
Random and Greedy Searchesp. 71
Hill Climbingp. 72
Simulated Annealingp. 73
Binary and Gray Codingp. 74
Step Sizes and Granularityp. 75
Optimizing with Real Numbersp. 77
Summaryp. 78
On Our Nonexistence as Entities: The Social Organismp. 81
Views of Evolutionp. 82
Gaia: The Living Earthp. 83
Differential Selectionp. 86
Our Microscopic Masters?p. 91
Looking for the Right Zoom Anglep. 92
Flocks, Herds, Schools, and Swarms: Social Behavior as Optimizationp. 94
Accomplishments of the Social Insectsp. 98
Optimizing with Simulated Ants: Computational Swarm Intelligencep. 105
Staying Together but Not Colliding: Flocks, Herds, and Schoolsp. 109
Robot Societiesp. 115
Shallow Understandingp. 125
Agencyp. 129
Summaryp. 131
Evolutionary Computation Theory and Paradigmsp. 133
Introductionp. 134
Evolutionary Computation Historyp. 134
The Four Areas of Evolutionary Computationp. 135
Genetic Algorithmsp. 135
Evolutionary Programmingp. 139
Evolution Strategiesp. 140
Genetic Programmingp. 141
Toward Unificationp. 141
Evolutionary Computation Overviewp. 142
EC Paradigm Attributesp. 142
Implementationp. 143
Genetic Algorithmsp. 146
An Overviewp. 146
A Simple GA Example Problemp. 147
A Review of GA Operationsp. 152
Schemata and the Schema Theoremp. 159
Final Comments on Genetic Algorithmsp. 163
Evolutionary Programmingp. 164
The Evolutionary Programming Procedurep. 165
Finite State Machine Evolutionp. 166
Function Optimizationp. 169
Final Commentsp. 171
Evolution Strategiesp. 172
Mutationp. 172
Recombinationp. 174
Selectionp. 175
Genetic Programmingp. 179
Summaryp. 185
Humans--Actual, Imagined, and Impliedp. 187
Studying Mindsp. 188
The Fall of the Behaviorist Empirep. 193
The Cognitive Revolutionp. 195
Bandura's Social Learning Paradigmp. 197
Social Psychologyp. 199
Lewin's Field Theoryp. 200
Norms, Conformity, and Social Influencep. 202
Sociocognitionp. 205
Simulating Social Influencep. 206
Paradigm Shifts in Cognitive Sciencep. 210
The Evolution of Cooperationp. 214
Explanatory Coherencep. 216
Networks in Groupsp. 218
Culture in Theory and Practicep. 220
Coordination Gamesp. 223
The El Farol Problemp. 226
Sugarscapep. 229
Tesfatsion's ACEp. 232
Picker's Competing-Norms Modelp. 233
Latane's Dynamic Social Impact Theoryp. 235
Boyd and Richerson's Evolutionary Culture Modelp. 240
Memeticsp. 245
Memetic Algorithmsp. 248
Cultural Algorithmsp. 253
Convergence of Basic and Applied Researchp. 254
Culture--and Life without Itp. 255
Summaryp. 258
Thinking Is Socialp. 261
Introductionp. 262
Adaptation on Three Levelsp. 263
The Adaptive Culture Modelp. 263
Axelrod's Culture Modelp. 265
Similarity in Axelrod's Modelp. 267
Optimization of an Arbitrary Functionp. 268
A Slightly Harder and More Interesting Functionp. 269
A Hard Functionp. 271
Parallel Constraint Satisfactionp. 273
Symbol Processingp. 279
Discussionp. 282
Summaryp. 284
The Particle Swarm and Collective Intelligence
The Particle Swarmp. 287
Sociocognitive Underpinnings: Evaluate, Compare, and Imitatep. 288
Evaluatep. 288
Comparep. 288
Imitatep. 289
A Model of Binary Decisionp. 289
Testing the Binary Algorithm with the De Jong Test Suitep. 297
No Free Lunchp. 299
Multimodalityp. 302
Minds as Parallel Constraint Satisfaction Networks in Culturesp. 307
The Particle Swarm in Continuous Numbersp. 309
The Particle Swarm in Real-Number Spacep. 309
Pseudocode for Particle Swarm Optimization in Continuous Numbersp. 313
Implementation Issuesp. 314
An Example: Particle Swarm Optimization of Neural Net Weightsp. 314
A Real-World Applicationp. 318
The Hybrid Particle Swarmp. 319
Science as Collaborative Searchp. 320
Emergent Culture, Immergent Intelligencep. 323
Summaryp. 324
Variations and Comparisonsp. 327
Variations of the Particle Swarm Paradigmp. 328
Parameter Selectionp. 328
Controlling the Explosionp. 337
Particle Interactionsp. 342
Neighborhood Topologyp. 343
Substituting Cluster Centers for Previous Bestsp. 347
Adding Selection to Particle Swarmsp. 353
Comparing Inertia Weights and Constriction Factorsp. 354
Asymmetric Initializationp. 357
Some Thoughts on Variationsp. 359
Are Particle Swarms Really a Kind of Evolutionary Algorithm?p. 361
Evolution beyond Darwinp. 362
Selection and Self-Organizationp. 363
Ergodicity: Where Can It Get from Here?p. 366
Convergence of Evolutionary Computation and Particle Swarmsp. 367
Summaryp. 368
Applicationsp. 369
Evolving Neural Networks with Particle Swarmsp. 370
Review of Previous Workp. 370
Advantages and Disadvantages of Previous Approachesp. 374
The Particle Swarm Optimization Implementation Used Herep. 376
Implementing Neural Network Evolutionp. 377
An Example Applicationp. 379
Conclusionsp. 381
Human Tremor Analysisp. 382
Data Acquisition Using Actigraphyp. 383
Data Preprocessingp. 385
Analysis with Particle Swarm Optimizationp. 386
Summaryp. 389
Other Applicationsp. 389
Computer Numerically Controlled Milling Optimizationp. 389
Ingredient Mix Optimizationp. 391
Reactive Power and Voltage Controlp. 391
Battery Pack State-of-Charge Estimationp. 391
Summaryp. 392
Implications and Speculationsp. 393
Introductionp. 394
Assertionsp. 395
Up from Social Learning: Bandurap. 398
Information and Motivationp. 399
Vicarious versus Direct Experiencep. 399
The Spread of Influencep. 400
Machine Adaptationp. 401
Learning or Adaptation?p. 402
Cellular Automatap. 403
Down from Culturep. 405
Soft Computingp. 408
Interaction within Small Groups: Group Polarizationp. 409
Informational and Normative Social Influencep. 411
Self-Esteemp. 412
Self-Attribution and Social Illusionp. 414
Summaryp. 419
And in Conclusion...p. 421
Statistics for Swarmersp. 429
Genetic Algorithm Implementationp. 451
Glossaryp. 457
Referencesp. 475
Indexp. 497
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