Adaptive velocity threshold particle swarm optimization
作者： Zhihua Cui , Jianchao Zeng , Guoji Sun
DOI: 10.1007/11795131_47
关键词: Benchmark (computing) 、 Multi-swarm optimization 、 Artificial neural network 、 Evolutionary algorithm 、 Mathematical optimization 、 Particle swarm optimization 、 Swarm intelligence 、 Metaheuristic 、 Mathematics 、 Rough set
摘要: Particle swarm optimization (PSO) is a new robust intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still works need do. Inspired by evolution programming theory, this paper proposes adaptive particle in velocity threshold dynamically changes during course of simulation. Seven benchmark functions used testify algorithm, and results showed clearly PSO leads significantly better performance, although were found be dependent problems
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