A New Optimizer Using Particle Swarm Theory

Eberhart, R.,Kennedy, J.

Nagoya（1995）

Purdue Sch. of Eng. & Technol.

被引用24192|浏览270

摘要

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed

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关键词

algorithm theory,feedforward neural nets,genetic algorithms,intelligent control,learning (artificial intelligence),multilayer perceptrons,optimisation,artificial life,benchmark testing,bird flocks,evolutionary computation,gbest,genetic algorithms,globally oriented concept,hyperspace,lbest,locally oriented paradigm,multilayer perceptron,neural network training,nonlinear functions,optimization,particle swarm theory,pbest,robot task learning