A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques
- 9 December 2014
- journal article
- Published by Elsevier BV in Applied Soft Computing
- Vol. 28, 138-149
- https://doi.org/10.1016/j.asoc.2014.11.018
Abstract
No abstract availableKeywords
Funding Information
- Science Fund for Hundred Excellent Innovation Talents Support Program of Hebei Province
- Doctoral Fund of Ministry of Education of China (20121333110008)
- Hebei Province Applied Basis Research Project (13961806D)
- Hebei Province Development of Social Science Research Project (201401315)
- National Natural Science Foundation of China (61273260, 61290322, 61273222, 61322303)
This publication has 15 references indexed in Scilit:
- Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logicExpert Systems with Applications, 2013
- An adaptive parameter tuning of particle swarm optimization algorithmApplied Mathematics and Computation, 2013
- A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimumApplied Mathematics and Computation, 2012
- A novel particle swarm optimization algorithm with adaptive inertia weightApplied Soft Computing, 2011
- Editorial survey: swarm intelligence for data miningMachine Learning, 2010
- On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systemsApplied Soft Computing, 2008
- A decreasing inertia weight particle swarm optimizerEngineering Optimization, 2007
- Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration CoefficientsIEEE Transactions on Evolutionary Computation, 2004
- Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless CommunicationScience, 2004
- Objective function “stretching” to alleviate convergence to local minimaNonlinear Analysis, 2001