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The new paradigm of Swarm Intelligence, called Particle Swarm
Optimization (PSO), is one of the well-known evolutionary
computation techniques, which can be considered as an efficient
tool to find near optimal solution in a searching space.
Especially, PSO is a useful method when the problems to be solved
are high-dimensional, nonlinear or some specific information is
unavailable. PSO combines the social-only model and the
cognition-only model to select the adjustable parameters to
approach optimal solution, its main advantage is its rapid
convergence and small computational requirements, which make it a
good candidate for solving optimization problems. In this book, the
efficient, robust, and flexible PSO algorithms are proposed to
generate some artificial intelligence system in solving some
applications, such as cluster analysis, image processing, and
neural network training.
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