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[null,"1.","Record Nr.",null,"UNINA9910701469603321"]
[null,null,"Titolo",null,"The future of national defense and the U.S. military ten years after 9/11 [[electronic resource] ] : perspectives from former service chiefs and vice chiefs : Committee on Armed Services, House of Representatives, One Hundred Twelfth Congress, first session, hearing held October 4, 2011"]
[null,null,"Pubbl/distr/stampa",null,"Washington : , : U.S. G.P.O., , 2012"]
[null,null,"Descrizione fisica",null,"1 online resource (iii, 62 pages) : illustrations"]
[null,null,"Soggetti",null,"National security - United States - Forecasting"]
[{"$nulls":4},"Terrorism - Prevention - Government policy - United States"]
[{"$nulls":4},"September 11 Terrorist Attacks, 2001 - Influence"]
[{"$nulls":4},"United States Defenses Forecasting"]
[{"$nulls":4},"United States Military policy Forecasting"]
[{"$nulls":4},"United States Armed Forces Appropriations and expenditures"]
[{"$nulls":4},"United States Armed Forces Operational readiness"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Paper version available for sale by the Supt. of Docs., U.S. G.P.O."]
[{"$nulls":4},"\"H.A.S.C. no. 112-72.\""]
[null,"2.","Record Nr.",null,"UNINA9910298965503321"]
[null,null,"Autore",null,"Tan Ying"]
[null,null,"Titolo",null,"Fireworks Algorithm :  A Novel Swarm Intelligence Optimization Method / / by Ying Tan"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-662-46353-9"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (344 p.)"]
[null,null,"Disciplina",null,"004"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Numerical analysis"]
[{"$nulls":4},"Robotics"]
[{"$nulls":4},"Automation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Numeric Computing"]
[{"$nulls":4},"Robotics and Automation"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references at the end of each chapters and index."]
[null,null,"Nota di contenuto",null,"Preface; Acknowledgments; Contents; About the Author; Abbreviations; Symbols; List of Figures; List of Tables; Part I Fundamentals and Basic Theory; 1 Introduction; 1.1 Motivations; 1.2 Brief Introduction to Swarm Intelligence; 1.3 Brief Introduction to FWA; 1.4 Characteristics and Advantages of FWA; 1.5 Overviews of FWA Research; 1.6 Overview of the Book; References; 2 Fireworks Algorithm (FWA); 2.1 Introduction; 2.2 FWA Principle; 2.2.1 Explosion Operator; 2.2.2 Gaussian Mutation Operator; 2.2.3 Mapping Rule; 2.2.4 Selection Strategy; 2.3 Implementation of FWA; 2.3.1 Explosion Operator"]
[{"$nulls":4},"2.3.2 Mutation Operator2.3.3 Mapping Rule; 2.3.4 Selection Strategy; 2.4 The Characteristics of FWA; 2.4.1 Explosion; 2.4.2 Instantaneity; 2.4.3 Simplicity; 2.4.4 Locality; 2.4.5 Emergent Property; 2.4.6 Distributed Parallelism; 2.4.7 Diversity; 2.4.8 Extendibility; 2.4.9 Adaptability; 2.5 Impact of Operators in FWA on Performance; 2.5.1 Explosion Operator; 2.5.2 Gaussian Mutation; 2.5.3 Mapping Rule; 2.5.4 Selection Strategy; 2.6 Comparison of FWA with Three Other SI "]
[{"$nulls":4},"Algorithms; 2.6.1 Ideas Comparison Between FWA and GA; 2.6.2 Ideas Comparison Between FWA and Two Versions  of PSO"]
[{"$nulls":4},"2.7 Experimental Results and Analysis2.7.1 Benchmark Functions; 2.7.2 Parameters Setting; 2.7.3 Experimental Results; 2.7.4 Analysis; 2.8 Summary; References; 3 Modeling and Theoretical Analysis of FWA; 3.1 A Stochastic Process Model for FWA; 3.2 Global Convergence Theorems; 3.3 Time Complexity of FWA; 3.3.1 Basic Theory of Time Complexity; 3.4 Deep Analysis of Time Complexity; 3.5 Influence of Random Number Generators on FWA; 3.5.1 Random Number Generators; 3.5.2 Modular Arithmetic Based RNGs; 3.5.3 Binary Arithmetic Based RNGs; 3.5.4 Experimental Setup"]
[{"$nulls":4},"3.5.5 Experimental Results and Analysis3.6 Summary; References; Part II FWA Variants; 4 FWA Based on Function Approximation Approaches; 4.1 Introduction; 4.2 Fireworks Algorithm; 4.3 Fireworks Algorithm Acceleration by Elite Strategy; 4.3.1 Motivation; 4.3.2 Sampling Methods; 4.3.3 Fireworks Algorithm with an Elite Strategy; 4.4 Experimental Evaluations; 4.4.1 Experimental Design; 4.4.2 Experimental Results; 4.5 Discussion; 4.5.1 Fireworks Algorithm Acceleration Performance; 4.5.2 Approximation Methods; 4.5.3 Sampling Methods; 4.5.4 Sampling Data Number; 4.6 Summary; References"]
[{"$nulls":4},"5 FWA with Controlling Exploration and Exploitation5.1 Some Improvements on Operations in FWA; 5.1.1 The Amplitude and Number of Sparks; 5.1.2 The Mutation Improvement; 5.1.3 Selection Strategy; 5.2 Experiment and Analysis; 5.2.1 Experimental Design; 5.2.2 Experimental Results and Analysis; 5.3 Summary; References; 6 Enhanced Fireworks Algorithm; 6.1 Properties of Conventional FWA; 6.2 The Proposed EFWA; 6.2.1 A New Minimal Explosion Amplitude Check (MEAC); 6.2.2 A New Operator for Generating Explosion Sparks; 6.2.3 A New Mapping Operator; 6.2.4 A New Operator for Generating Gaussian Sparks"]
[{"$nulls":4},"6.2.5 A New Selection Operator"]
[null,null,null,"Sommario/riassunto","This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modelling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metaheuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc."]