A modification to particle swarm optimization algorithm

By Hui-Yuan Fan. 2002

126 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/55173f3b782e76394a479b5773f6e72cd5749eda

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chun Lu
2. A. Chipperfield
3. M. E. H. Pedersen
4. H. P. Lee
5. Yanchun Liang
6. Lei Liu
7. Xiaohu Shi
8. Dongshu Wang

In Computational Theory and Mathematics

1. Chun Lu
2. A. Chipperfield
3. M. E. H. Pedersen
4. H. P. Lee
5. Yanchun Liang
6. Lei Liu
7. Xiaohu Shi
8. Dongshu Wang

In Electrical and Electronic Engineering

1. A. Chipperfield
2. M. E. H. Pedersen
3. Lei Liu
4. Dongshu Wang
5. Dapei Tan

Citations in the corpus

• Simplifying Particle Swarm Optimization (2010, methodology): “Another PSO variant for optimizing ANN weights is introduced by Fan [13], and makes its particles velocity-boundaries more narrow for each iteration of the algorithm, in an attempt to focus the search more towards the end of the optimization run.”
• Particle swarm optimization algorithm: an overview (2017, background): “Generallyspeaking, V max wassettothedynamicrange of each variable, and usually a fixed value, but it could also linearly decrease with time (Fan 2002) or dynamically reduce according to the success of search history (Fourie and Groen-wold 2002).”
• An improved GA and a novel PSO-GA-based hybrid algorithm (2005, background): “Particle Swarm Optimization (PSO) also is an e lutionary computational model which is based swarm intelligence.”