作者:
J Kennedy，R Eberhart
关键词:
artificial intelligence；genetic algorithms；neural nets；search problems；simulation；artificial life；evolution；genetic algorithms；multidimensional search；neural network
摘要:
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described
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