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| 《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》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。 |
| 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。 |
| part one Foundations chapter one Models and Concepts of Life and Intelligence The Mechanics of Life and Thought Stochastic Adaptation： Is Anything Ever Really Random? The “Two Great Stochastic Systems” The Game of Life： Emergence in Complex Systems The Game of Life Emergence Cellular Automata and the Edge of Chaos Artificial Life in Computer Programs Intelligence： Good Minds in People and Machines Intelligence in People： The Boring Criterion Intelligence in Machines： The Turing Criterion chapter two Symbols， Connections， and Optimization by Trial and Error Symbols in Trees and Networks Problem Solving and Optimization A Super-Simple Optimization Problem Three Spaces of Optimization Fitness Landscapes High-Dimensional Cognitive Space and Word Meanings Two Factors of Complexity： NK Landscapes Combinatorial Optimization Binary Optimization Random and Greedy Searches Hill Climbing Simulated Annealing Binary and Gray Coding Step Sizes and Granularity Optimizing with Real Numbers Summary chapter three On Our Nonexistence as Entities： The Social Organism Views of Evolution Gaia： The Living Earth Differential Selection Our Microscopic Masters? Looking for the Right Zoom Angle Flocks， Herds， Schools， and Swarms： Social Behavior as Optimization Accomplishments of the Social Insects Optimizing with Simulated Ants： Computational Swarm Intelligence Staying Together but Not Colliding： Flocks， Herds， and Schools Robot Societies Shallow Understanding Agency Summary chapter four Evolutionary Computation Theory and Paradigms Introduction Evolutionary Computation History The Four Areas of Evolutionary Computation Genetic Algorithms Evolutionary Programming Evolution Strategies Genetic Programming Toward Unification Evolutionary Computation Overview EC Paradigm Attributes Implementation Genetic Algorithms An Overview A Simple GA Example Problem A Review of GA Operations Schemata and the Schema Theorem Final Comments on Genetic Algorithms Evolutionary Programming The Evolutionary Programming Procedure Finite State Machine Evolution Function Optimization Final Comments Evolution Strategies Mutation Recombination Selection Genetic Programming Summary chapter five Humans-Actual， Imagined， and Implied Studying Minds The Fall of the Behaviorist Empire The Cognitive Revolution Banduras Social Learning Paradigm Social Psychology Lewins Field Theory Norms， Conformity， and Social Influence Sociocognition Simulating Social Influence Paradigm Shifts in Cognitive Science The Evolution of Cooperation Explanatory Coherence Networks in Groups Culture in Theory and Practice Coordination Games The El Farol Problem Sugarscape Tesfatsions ACE Pickers Competing-Norms Model Latanés Dynamic Social Impact Theory Boyd and Richersons Evolutionary Culture Model Memetics Memetic Algorithms Cultural Algorithms Convergence of Basic and Applied Research Culture-and Life without It Summary chapter six Thinking Is Social Introduction Adaptation on Three Levels The Adaptive Culture Model Axelrods Culture Model Experiment One： Similarity in Axelrods Model Experiment Two： Optimization of an Arbitrary Function Experiment Three： A Slightly Harder and More Interesting Function Experiment Four： A Hard Function Experiment Five： Parallel Constraint Satisfaction Experiment Six： Symbol Processing Discussion Summary part two The Particle Swarm and Collective Intelligence chapter seven The Particle Swarm Sociocognitive Underpinnings： Evaluate， Compare， and Imitate Evaluate Compare Imitate A Model of Binary Decision Testing the Binary Algorithm with the De Jong Test Suite No Free Lunch Multimodality Minds as Parallel Constraint Satisfaction Networks in Cultures The Particle Swarm in Continuous Numbers The Particle Swarm in Real-Number Space Pseudocode for Particle Swarm Optimization in Continuous Numbers Implementation Issues An Example： Particle Swarm Optimization of Neural Net Weights A Real-World Application The Hybrid Particle Swarm Science as Collaborative Search Emergent Culture， Immergent Intelligence Summary chapter eight Variations and Comparisons Variations of the Particle Swarm Paradigm Parameter Selection Controlling the Explosion Particle Interactions Neighborhood Topology Substituting Cluster Centers for Previous Bests Adding Selection to Particle Swarms Comparing Inertia Weights and Constriction Factors Asymmetric Initialization Some Thoughts on Variations Are Particle Swarms Really a Kind of Evolutionary Algorithm? Evolution beyond Darwin Selection and Self-Organization Ergodicity： Where Can It Get from Here? Convergence of Evolutionary Computation and Particle Swarms Summary chapter nine Applications Evolving Neural Networks with Particle Swarms Review of Previous Work Advantages and Disadvantages of Previous Approaches The Particle Swarm Optimization Implementation Used Here Implementing Neural Network Evolution An Example Application Conclusions Human Tremor Analysis Data Acquisition Using Actigraphy Data Preprocessing Analysis with Particle Swarm Optimization Summary Other Applications Computer Numerically Controlled Milling Optimization Ingredient Mix Optimization Reactive Power and Voltage Control Battery Pack State-of-Charge Estimation Summary chapter ten Implications and Speculations Introduction Assertions Up from Social Learning： Bandura Information and Motivation Vicarious versus Direct Experience The Spread of Influence Machine Adaptation Learning or Adaptation? Cellular Automata Down from Culture Soft Computing Interaction within Small Groups： Group Polarization Informational and Normative Social Influence Self-Esteem Self-Attribution and Social Illusion Summary chapter eleven And in Conclusion Appendix A Statistics for Swarmers Appendix B Genetic Algorithm Implementation Glossary References Index |
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