中心粒子群优化算法

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Pub Date : 2019-03-15 DOI:10.1109/ITNEC.2019.8729510

Y. Xiaojing, Ji Qingju, Liu Xinke

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引用次数: 24

摘要

在中心粒子概念的基础上，提出了线性减权粒子群优化算法(LDWPSO)，并提出了中心粒子群优化算法(PSO)。线性减权粒子群优化算法与其他一般中心粒子不同，粒子速度中心不明确，且始终位于粒子群的中心。此外，将神经网络训练算法与粒子群优化算法和线性减权粒子群优化算法进行比较，结果表明:性能优于线性优化中心粒子群减权粒子群优化算法。算法。

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Center Particle Swarm Optimization Algorithm

The linear decreasing weight particle swarm optimization algorithm (LDWPSO) is mentioned in the concept of a center particle, and then puts forward center particle swarm optimization algorithm (PSO). The linear decreasing weight particle swarm optimization algorithm, unlike other general center particle, particle velocity center is not clear, and is always placed in the center of the particle swarm. In addition, the neural network training algorithm compared to particle swarm optimization algorithm and the linear decreasing weight particle swarm optimization algorithm, results show that: the performance is better than the linear optimization center particle swarm decreasing weight PSO algorithm. algorithm.

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来源期刊

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

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