@inproceedings{b22cdc4d61614fbdb894d1f11d0b592f,
title = "Self-active inertia weight strategy in particle swarm optimization algorithm",
abstract = "Inertia weight is one of the most important parameters of particle swarm optimization (PSO) algorithm. We introduce s self-active inertia weight strategy, in which the inertia weight is updated according to the convergence rate of the search process related to the optimized function. Four different functions were used to evaluate the effects of these strategies on the PSO performance. The experimental results show that self-active strategy is significantly faster convergence than LPSO.",
keywords = "Inertia weight, Particle swarm optimization",
author = "Chen Guimin and Min Zhengfeng and Jia Jianyuan and Xinbo Huang",
year = "2006",
doi = "10.1109/WCICA.2006.1713058",
language = "英语",
isbn = "1424403324",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
pages = "3686--3689",
booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",
}