Particle Swarm Optimisation

By Xiaojun Wu. 2011

670 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/99a491db137ae8e1971149ccb68ec35a5cc47da5

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. Xingshi He
2. Xin-She Yang
3. Amita Mahor
4. Saroj Rangnekar
5. Vishnu Prasad
6. Arpan Kumar Kar
7. Seyed Mohammad Mirjalili
8. Seyedali Mirjalili

In Computer Networks and Communications

1. Amita Mahor
2. Xin-She Yang
3. Saroj Rangnekar
4. Vishnu Prasad
5. Xingshi He
6. Seyed Mohammad Mirjalili
7. Sankalap Arora
8. Heitor Silvério Lopes

Citations in the corpus

• Economic dispatch using particle swarm optimization: A review (2009, methodology): “It was first introduced by Kennedy and Eberhart in 1995 [61] as new heuristic method.”
• Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design (2013, background): “Examples of stochastic algorithms that are designed to deal with highly complex optimisation problems are [119]: annealing [120-122], tabu search [123], ant colony [124], particle swarm [125] and genetic algorithms [126-128].”
• Firefly Algorithm: Recent Advances and Applications (2013, methodology): “For example, particle swarm optimisation was based on the swarming behaviour of birds and fish [24], while the firefly algorithm was based on the flashing pattern of tropical fireflies [32, 33] and cuckoo search algorithm was inspired by the brood parasitism of some cuckoo species [37].”