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Language:Chinese.No Binding.publisher:Tsinghua University Press Pub. Date :2009-10-1.description:Pages Number: 423 Publisher: Tsinghua University Press Pub. Date :2009-10-1. Book is a comprehensive and systematic introduction to swarm intelligence in the calculation of particle swarm optimization (pso) and ant colony optimization (aco) of the ba
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Calculated based on swarm intelligence
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Tsinghua University Press Pub. Date :2009-10-1, 2000
ISBN 10: 7302208964
ISBN 13: 9787302208969
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Da: liu xing, Nanjing, JS, Cina
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paperback. Condizione: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Pages Number: 423 Publisher: Tsinghua University Press Pub. Date :2009-10-1. Book is a comprehensive and systematic introduction to swarm intelligence in the calculation of particle swarm optimization (pso) and ant colony optimization (aco) of the basic concepts. basic model. theoretical analysis and its applications. In brief summary of the basic types of optimization theory and optimization problems. focuses on how social network structure and the exchange of information between individuals how individuals act together to form a powerful organism. After the overview of evolutionary computation. with emphasis on particle swarm optimization and ant colony optimization of the basic model and its variations. given the analysis of particle swarm optimization model of a generic method that achieved based on ant colony behavior optimization algorithm and used to solve practical problems. This book can serve as institutions of higher learning intelligent science. computer. automation. electronic information. communication. pattern recognition and other professional graduate and senior undergraduate textbook. but also as an intelligent information processing. swarm intelligence and engineering technology related professional engineering staff reference book. Contents: Chapter 1 Introduction The first part of the Chapter 2. optimization theory and methods of optimization for unconstrained optimization Chapter 3 Chapter 4 Chapter 5 constrained optimization problem of multiple solutions for multi-objective optimization Chapter 6 Chapter 7 of the second part of the evolution of dynamic optimization problems calculated in Chapter 8 Chapter 9 Introduction to Evolutionary Computation Evolutionary Computation Methods Chapter 10 co-evolutionary particle swarm optimization of the third part of Chapter 11 Introduction Chapter 12 Chapter 13 Particle swarm optimization particle trajectories Chapter 14 Section 15 Proof of convergence Chapter one solution PSO niching Chapter 16 Chapter 17 of the particle swarm optimization particle swarm optimization using constrained particle swarm optimization Chapter 18 Chapter 19 multi-objective optimization in a dynamic environment particle swarm optimization Chapter 20 of the first discrete particle swarm optimization Chapter 21 Application of particle swarm optimization algorithm for the fourth part of the ant Chapter 22 Introduction Chapter 23 ant colony optimization meta-heuristic algorithm ant colony optimization algorithm Chapter 24 Chapter 25 of the general framework of the ant colony optimization algorithm for Chapter 26 ant colony algorithm application of collective decision-making Chapter 27 Chapter 28 The convergence of ant colony optimization cemetery Chapter 29 Chapter 30 Organization and brood division Chapter 31 Epilogue References Appendix a high-level reading material appendix b acronyms symbol indexFour Satisfaction guaranteed,or money back. Codice articolo L98799
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