Global optimization is an important area of applied mathematics that has shown fast and growing development during the last few decades, and deals with the optimization of a function or a set of functions according to some criteria. In this book the authors discuss the theory, developments and applications of global optimization including: particle swarm optimization with re-initialization strategies for continuous global optimization; particle swarm global optimization for orbital maneuvers; float-encoded genetic algorithms used for the inversion processing of well-logging data; particle collision algorithms; classifier-assisted frameworks for computationally expensive optimization problems; and the cutting box strategy as an algorithmic framework for improving metaheuristics for continuous global optimization.
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