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 modeling 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 metahuristics,
swarm intelligence, evolutionary computation, complex optimization
solving, etc.
