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.

Reviews the state-of-the-art of firework algorithms (FWA) as a novel explosive search way for optimization Offers the key operators and characteristics as well as theoretical analyses of convergence and time-complexity of FWA through stochastic Markov process Presents exhaustively the key recent research into varieties of improving versions of FWA so far Enriches understanding of FWA by incorporating FWA with GPU, MOO, and combinatorial optimization Covers many different applications including NMF, document clustering, pattern recognition, inversion problem, and swarm robotics Includes supplementary material: sn.pub/extras