LEADER 06551nam 22007335 450 001 9910298965503321 005 20200703160503.0 010 $a3-662-46353-9 024 7 $a10.1007/978-3-662-46353-6 035 $a(CKB)3710000000486869 035 $a(EBL)4178856 035 $a(SSID)ssj0001584251 035 $a(PQKBManifestationID)16264574 035 $a(PQKBTitleCode)TC0001584251 035 $a(PQKBWorkID)14864370 035 $a(PQKB)10118865 035 $a(DE-He213)978-3-662-46353-6 035 $a(MiAaPQ)EBC4178856 035 $a(PPN)190515309 035 $a(EXLCZ)993710000000486869 100 $a20151011d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFireworks Algorithm $e A Novel Swarm Intelligence Optimization Method /$fby Ying Tan 205 $a1st ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (344 p.) 300 $aDescription based upon print version of record. 311 $a3-662-46352-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aPreface; 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 327 $a2.3.2 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 Algorithms; 2.6.1 Ideas Comparison Between FWA and GA; 2.6.2 Ideas Comparison Between FWA and Two Versions of PSO 327 $a2.7 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 327 $a3.5.5 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 327 $a5 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 327 $a6.2.5 A New Selection Operator 330 $aThis 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. 606 $aArtificial intelligence 606 $aComputational intelligence 606 $aNumerical analysis 606 $aRobotics 606 $aAutomation 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aNumeric Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I1701X 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 615 0$aNumerical analysis. 615 0$aRobotics. 615 0$aAutomation. 615 14$aArtificial Intelligence. 615 24$aComputational Intelligence. 615 24$aNumeric Computing. 615 24$aRobotics and Automation. 676 $a004 700 $aTan$b Ying$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846863 906 $aBOOK 912 $a9910298965503321 996 $aFireworks Algorithm$92534951 997 $aUNINA