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[null,"1.","Record Nr.",null,"UNINA9910143311203321"]
[null,null,"Autore",null,"Clerc Maurice"]
[null,null,"Titolo",null,"Particle swarm optimization  / / Maurice Clerc"]
[null,null,"Pubbl/distr/stampa",null,"London ; ; Newport Beach : , : ISTE, , 2006"]
[{"$nulls":4},"©2006"]
[null,null,"ISBN",null,"9780470612163"]
[{"$nulls":4},"9786610510566"]
[{"$nulls":4},"1-84704-454-9"]
[{"$nulls":4},"0-470-61216-9"]
[{"$nulls":4},"0-470-39443-9"]
[{"$nulls":4},"1-84704-554-5"]
[null,null,"Descrizione fisica",null,"1 online resource (245 pages)"]
[null,null,"Collana",null,"ISTE ; ; v.93"]
[null,null,"Disciplina",null,"006.3"]
[{"$nulls":4},"519.62"]
[null,null,"Soggetti",null,"Mathematical optimization"]
[{"$nulls":4},"Particles (Nuclear physics)"]
[{"$nulls":4},"Swarm intelligence"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. [233]-238) and index."]
[null,null,"Nota di contenuto",null,"Particle Swarm Optimization; Table of Contents; Foreword; Introduction; Part I. Particle Swarm Optimization; Chapter 1. What is a Difficult Problem?; 1.1. An intrinsic definition.; 1.2. Estimation and practical measurement.; 1.3. For \"amatheurs\": some estimates of difficulty; 1.3.1. Function xd; 1.3.2. Function x2d; 1.3.3. Function xd|sin(xd)|; 1.3.4. Traveling salesman on D cities; 1.4. Summary; Chapter 2. On a Table Corner; 2.1. Apiarian metaphor; 2.2. An aside on the spreading of a rumor; 2.3. Abstract formulation; 2.4. What is really transmitted; 2.5. Cooperation versus competition; 2.6. For \"amatheurs\": a simple calculation of propagation of rumor2.7. Summary; Chapter 3. First Formulations; 3.1. Minimal version; 3.1.1. Swarm size; 3.1.2. Information links; 3.1.3. Initialization; 3.1.4. Equations of motion; 3.1.5. Interval confinement; 3.1.6. Proximity distributions; 3.2. Two common errors; 3.3. Principal drawbacks of this formulation; 3.3.1. Distribution bias; 3.3.2. Explosion and maximum velocity; 3.4. Manual parameter setting; 3.5. For \"amatheurs\": average number of informants; "]
[{"$nulls":4},"3.6. Summary; Chapter 4. Benchmark Set; 4.1. What is the purpose of test functions?; 4.2. Six reference functions4.3. Representations and comments; 4.4. For \"amatheurs\": estimates of levels of difficulty; 4.4.1. Theoretical difficulty; 4.4.1.1. Tripod; 4.4.1.2. Alpine 10D; 4.4.1.3. Rosenbrock; 4.4.2. Difficulty according to the search effort; 4.5. Summary; Chapter 5. Mistrusting Chance; 5.1. Analysis of an anomaly; 5.2. Computing randomness; 5.3. Reproducibility; 5.4. On numerical precision; 5.5. The rare KISS; 5.5.1. Brief description; 5.5.2. Test of KISS; 5.6. On the comparison of results; 5.7. For \"amatheurs\": confidence in the estimate of a rate of failure; 5.8. C programs5.9. Summary; Chapter 6. First Results; 6.1. A simple program; 6.2. Overall results; 6.3. Robustness and performance maps; 6.4. Theoretical difficulty and noted difficulty; 6.5. Source code of OEP 0; 6.6. Summary; Chapter 7. Swarm: Memory and Graphs of Influence; 7.1. Circular neighborhood of the historical PSO; 7.2. Memory-swarm; 7.3. Fixed topologies; 7.4. Random variable topologies; 7.4.1. Direct recruitment; 7.4.2. Recruitment by common channel of communication; 7.5. Influence of the number of informants; 7.5.1. In fixed topology; 7.5.2. In random variable topology; 7.6. Influence of the number of memories7.7. Reorganizations of the memory-swarm; 7.7.1. Mixing of the memories; 7.7.2. Queen and other centroids; 7.7.3. Comparative results; 7.8. For \"amatheurs\": temporal connectivity in random recruitment; 7.9. Summary; Chapter 8. Distributions of Proximity; 8.1. The random possibilities; 8.2. Review of rectangular distribution; 8.3. Alternative distributions of possibilities; 8.3.1. Ellipsoidal positive sectors; 8.3.2. Independent Gaussians; 8.3.3. Local by independent Gaussians; 8.3.4. The class of one-dimensional distributions; 8.3.5. Pivots; 8.3.6. Adjusted ellipsoids"]
[null,null,null,"Sommario/riassunto","This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization."]