Fireworks Algorithm [electronic resource] :  A Novel Swarm Intelligence Optimization Method /
Tan, Ying.author.
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.
Description based upon print version of record.
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
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 Algorithms; 2.6.1 Ideas Comparison Between FWA and GA; 2.6.2 Ideas Comparison Between FWA and Two Versions  of PSO
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
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
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
6.2.5 A New Selection Operator
Includes bibliographical references at the end of each chapters and index.
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.
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