| 词条 | 群体智能 |
| 释义 | 群体智能这个概念来自对自然界中昆虫群体的观察，群居性生物通过协作表现出的宏观智能行为特征被称为群体智能。 群体智能(Swarm intelligence):群体智能五条基本原则(1) 邻近原则( Proximity Principle) ,群体能够进行简单的空间和时间计算; (2) 品质原则(Quality Principle) ,群体能够响应环境中的品质因子; (3) 多样性反应原则( Principle of Diverse Re2sponse) ,群体的行动范围不应该太窄; (4) 稳定性原则(Stability Principle) ,群体不应在每次环境变化时都改变自身的行为; (5) 适应性原则(Adaptability Principle) ,在所需代价不太高的情况下,群体能够在适当的时候改变自身的行为。 群体智能特点(1) 控制是分布式的,不存在中心控制。因而它更能够适应当前网络环境下的工作状态,并且具有较强的鲁棒性,即不会由于某一个或几个个体出现故障而影响群体对整个问题的求解。 (2) 群体中的每个个体都能够改变环境,这是个体之间间接通信的一种方式,这种方式被称为“激发工作”(Stigmergy) 。由于群体智能可以通过非直接通信的方式进行信息的传输与合作,因而随着个体数目的增加,通信开销的增幅较小,因此,它具有较好的可扩充性。 (3) 群体中每个个体的能力或遵循的行为规则非常简单,因而群体智能的实现比较方便,具有简单性的特点。 (4) 群体表现出来的复杂行为是通过简单个体的交互过程突现出来的智能( Emergent Intelli2gence) ,因此,群体具有自组织性。 典型的群体智能模型群体智能的相关研究早已存在，到目前为止也取得了许多重要的结果。自1991年意大利学者Dorigo 提出蚁群优化（Ant Colony Optimization，ACO）理论开始，群体智能作为一个理论被正式提出，并逐渐吸引了大批学者的关注，从而掀起了研究高潮。1995年，Kennedy 等学者提出粒子群优化算法（Particle Swarm Opti -mization，PSO ），此后群体智能研究迅速展开，但大部分工作都是围绕ACO和PSO 进行的。 目前群智能研究主要包括智能蚁群算法和粒子群算法。智能蚁群算法主要包括蚁群优化算法、蚁群聚类算法和多机器人协同合作系统。其中，蚁群优化算法和粒子群优化算法在求解实际问题时应用最为广泛。 英文版.群体智能作者:(美国)James Kennedy (美国)Russell C Eberhart,Yuhui Shi(史玉回) 市场价：¥75.00 出版社：人民邮电出版社 页码：512 页 出版日期：2009年 ISBN：9787115195500 版本：1版 装帧：平装 开本：16 英语：英语 丛书名：图灵原版计算机科学系列 内容简介群体智能是通过模拟自然界生物群体行为来实现人工智能的一种方法。《群体智能》综合运用认知科学、社会心理学、人工智能和演化计算等学科知识，提供了一些非常有价值的新见解，并将这些见解加以应用，以解决困难的工程问题。书中首先探讨了基础理论，然后详尽展示如何将这些理论和模型应用于新的计算智能方法(粒子群)中，以适应智能系统的行为，最后描述了应用粒子群优化算法的好处，提供了强有力的优化、学习和问题解决的方法。 《群体智能》主要面向计算机相关学科的高年级本科生或研究生以及相关领域的研究与开发技术人员。 作者简介James Kennedy社会心理学家。自1994年起，他一直致力于粒子群算法的研究工作，并与Russell C．Eberhart共同开发了粒子群优化算法。目前在美国劳工部从事调查方法的研究工作。他在计算机科学和社会科学杂志和学报上发表过许多关于粒子群的论文。 RusselI C．Eberhart 普度大学电子与计算机工程系主任。IEEE会士。与JamesKennedy共同提出了粒子群优化算法。曾任IEEE神经网络委员会的主席。除了本书之外，他还著有《计算智能：从概念到实现》（影印版由人民邮电出版社出版）等。 Yuhui Shi (史玉回)国际计算智能领域专家，现任Joumal ofSwarm Intellgence编委，IEEE CIS群体智能任务组主席，西交利物浦大学电子与电气工程系教授。1992年获东南大学博士学位，先后在美国、韩国、澳大利亚等地从事研究工作，曾任美国电子资讯系统公司专家长达9年。他还是《计算智能：从概念到实现》一书的作者之一。 媒体推荐“本书内容丰富，富于启发性和思想性，强烈推荐给所有的演进计算研究人员。” ——Genetic Programming and Evolvable'Machines “这本书极为出色，不愧为PSO和群体智能的最佳参考书：” ——Konstantions E.Parsopoulos 希腊Palras大学 编辑推荐群体智能是近年来发展迅速的人工智能学科领域。通过研究分散、自组织的动物群体和人类社会的智能行为，学者们提出了许多迥异于传统思路的智能算法，很好地解决了不少原来非常棘手的复杂工程问题。与蚁群算法齐名的粒子群优化（particle swarm optimizatiotl，简称PSO）算法就是其中最受瞩目、应用最为广泛的成果之一。 《群体智能》由粒子群优化算法之父撰写，是该领域毋庸置疑的经典著作。作者提出，人类智能来源于社会环境中个体之间的交互，这种智能模型可以有效地应用到人工智能系统中去。书中首先从社会心理学、认知科学和演化计算等多个角度阐述了这种新方法的基础，然后详细说明了应用这些理论和模型所得出的新的计算智能方法——粒子群优化，进而深入地探讨了如何将粒子群优化应用于广泛的工程问题。 《群体智能》的C及ViSLlaI Basic源代码可以在图灵网站《群体智能》网页免费注册下载。 目录part one Foundations chapter one Models and Concepts of Life and Intelligence 3 The Mechanics of Life and Thought 4 Stochastic Adaptation: Is Anything Ever Really Random? 9 The “Two Great Stochastic Systems” 12 The Game of Life: Emergence in Complex Systems 16 The Game of Life 17 Emergence 18 Cellular Automata and the Edge of Chaos 20 Artificial Life in Computer Programs 26 Intelligence: Good Minds in People and Machines 30 Intelligence in People: The Boring Criterion 30 Intelligence in Machines: The Turing Criterion 32 chapter two Symbols, Connections, and Optimization by Trial and Error 35 Symbols in Trees and Networks 36 Problem Solving and Optimization 48 A Super-Simple Optimization Problem 49 Three Spaces of Optimization 51 Fitness Landscapes 52 High-Dimensional Cognitive Space and Word Meanings 55 Two Factors of Complexity: NK Landscapes 60 Combinatorial Optimization 64 Binary Optimization 67 Random and Greedy Searches 71 Hill Climbing 72 Simulated Annealing 73 Binary and Gray Coding 74 Step Sizes and Granularity 75 Optimizing with Real Numbers 77 Summary 78 chapter three On Our Nonexistence as Entities: The Social Organism 81 Views of Evolution 82 Gaia: The Living Earth 83 Differential Selection 86 Our Microscopic Masters? 91 Looking for the Right Zoom Angle 92 Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94 Accomplishments of the Social Insects 98 Optimizing with Simulated Ants: Computational Swarm Intelligence 105 Staying Together but Not Colliding: Flocks, Herds, and Schools 109 Robot Societies 115 Shallow Understanding 125 Agency 129 Summary 131 chapter four Evolutionary Computation Theory and Paradigms 133 Introduction 134 Evolutionary Computation History 134 The Four Areas of Evolutionary Computation 135 Genetic Algorithms 135 Evolutionary Programming 139 Evolution Strategies 140 Genetic Programming 141 Toward Unification 141 Evolutionary Computation Overview 142 EC Paradigm Attributes 142 Implementation 143 Genetic Algorithms 146 An Overview 146 A Simple GA Example Problem 147 A Review of GA Operations 152 Schemata and the Schema Theorem 159 Final Comments on Genetic Algorithms 163 Evolutionary Programming 164 The Evolutionary Programming Procedure 165 Finite State Machine Evolution 166 Function Optimization 169 Final Comments 171 Evolution Strategies 172 Mutation 172 Recombination 174 Selection 175 Genetic Programming 179 Summary 185 chapter five Humans—Actual, Imagined, and Implied 187 Studying Minds 188 The Fall of the Behaviorist Empire 193 The Cognitive Revolution 195 Bandura’s Social Learning Paradigm 197 Social Psychology 199 Lewin’s Field Theory 200 Norms, Conformity, and Social Influence 202 Sociocognition 205 Simulating Social Influence 206 Paradigm Shifts in Cognitive Science 210 The Evolution of Cooperation 214 Explanatory Coherence 216 Networks in Groups 218 Culture in Theory and Practice 220 Coordination Games 223 The El Farol Problem 226 Sugarscape 229 Tesfatsion’s ACE 232 Picker’s Competing-Norms Model 233 Latané’s Dynamic Social Impact Theory 235 Boyd and Richerson’s Evolutionary Culture Model 240 Memetics 245 Memetic Algorithms 248 Cultural Algorithms 253 Convergence of Basic and Applied Research 254 Culture—and Life without It 255 Summary 258 chapter six Thinking Is Social 261 Introduction 262 Adaptation on Three Levels 263 The Adaptive Culture Model 263 Axelrod’s Culture Model 265 Experiment One: Similarity in Axelrod’s Model 267 Experiment Two: Optimization of an Arbitrary Function 268 Experiment Three: A Slightly Harder and More Interesting Function 269 Experiment Four: A Hard Function 271 Experiment Five: Parallel Constraint Satisfaction 273 Experiment Six: Symbol Processing 279 Discussion 282 Summary 284 part two The Particle Swarm and Collective Intelligence chapter seven The Particle Swarm 287 Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288 Evaluate 288 Compare 288 Imitate 289 A Model of Binary Decision 289 Testing the Binary Algorithm with the De Jong Test Suite 297 No Free Lunch 299 Multimodality 302 Minds as Parallel Constraint Satisfaction Networks in Cultures 307 The Particle Swarm in Continuous Numbers 309 The Particle Swarm in Real-Number Space 309 Pseudocode for Particle Swarm Optimization in Continuous Numbers 313 Implementation Issues 314 An Example: Particle Swarm Optimization of Neural Net Weights 314 A Real-World Application 318 The Hybrid Particle Swarm 319 Science as Collaborative Search 320 Emergent Culture, Immergent Intelligence 323 Summary 324 chapter eight Variations and Comparisons 327 Variations of the Particle Swarm Paradigm 328 Parameter Selection 328 Controlling the Explosion 337 Particle Interactions 342 Neighborhood Topology 343 Substituting Cluster Centers for Previous Bests 347 Adding Selection to Particle Swarms 353 Comparing Inertia Weights and Constriction Factors 354 Asymmetric Initialization 357 Some Thoughts on Variations 359 Are Particle Swarms Really a Kind of Evolutionary Algorithm? 361 Evolution beyond Darwin 362 Selection and Self-Organization 363 Ergodicity: Where Can It Get from Here? 366 Convergence of Evolutionary Computation and Particle Swarms 367 Summary 368 chapter nine Applications 369 Evolving Neural Networks with Particle Swarms 370 Review of Previous Work 370 Advantages and Disadvantages of Previous Approaches 374 The Particle Swarm Optimization Implementation Used Here 376 Implementing Neural Network Evolution 377 An Example Application 379 Conclusions 381 Human Tremor Analysis 382 Data Acquisition Using Actigraphy 383 Data Preprocessing 385 Analysis with Particle Swarm Optimization 386 Summary 389 Other Applications 389 Computer Numerically Controlled Milling Optimization 389 Ingredient Mix Optimization 391 Reactive Power and Voltage Control 391 Battery Pack State-of-Charge Estimation 391 Summary 392 chapter ten Implications and Speculations 393 Introduction 394 Assertions 395 Up from Social Learning: Bandura 398 Information and Motivation 399 Vicarious versus Direct Experience 399 The Spread of Influence 400 Machine Adaptation 401 Learning or Adaptation? 402 Cellular Automata 403 Down from Culture 405 Soft Computing 408 Interaction within Small Groups: Group Polarization 409 Informational and Normative Social Influence 411 Self-Esteem 412 Self-Attribution and Social Illusion 414 Summary 419 chapter eleven And in Conclusion . . . 421 Appendix A Statistics for Swarmers 429 Appendix B Genetic Algorithm Implementation 451 Glossary 457 References 475 Index 497 …… 序言At this moment a half．dozen astronauts are assembling a new space station hundreds of miles above the surface of the earth．Thousands of sailors live and work under the sea in submarines．Incas iog through theAndes．Nomads roam the Arabian sands．Homo sapiensliterally,“intelli-gent man” has adapted to nearly every environment on the face of theearth．below it，and as far above it as we can propel ourselves．W_e must bedoing something right． In this book we argue that what we do right is related to our socialit．We will investigate that elusive quality known as intelligence，which isconsidered first of all a trait of humans and second as something thatmight be created in a computer,and our conclusion will be that whatever this“intelligence”is。it arises from interactions among individuals．We humans are the most social of animals：we live together in families，tribes．cities，nations，behaving and thinking according to the rules andnorms of our communities，adopting the customs of our fellows，including the facts they believe and the explanations they use to tie those factstogether．Even when we are alone，we think about other people，andeven when we think about inanimate things，we think using language the medium of interpersonal communication． |
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