Enhancing particle swarm optimization using generalized opposition-based learning
By Hui Wang, Zhijian Wu, Shahryar Rahnamayan, Yong Liu, Mario Ventresca. Information Sciences, 2011
456 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/9de932e234aee68bd12674ff7ead2aaef8f84313
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
- Konstantinos E. Parsopoulos
- Lei Liu
- Dongshu Wang
- Dapei Tan
- Yudong Zhang
- Genlin Ji
- Shuihua Wang
- Shahryar Rahnamayan
In Computational Theory and Mathematics
- Konstantinos E. Parsopoulos
- Lei Liu
- Dongshu Wang
- Dapei Tan
- Yudong Zhang
- Genlin Ji
- Shuihua Wang
- Shahryar Rahnamayan
Citations in the corpus
- Particle Swarm Methods (2018, methodology): “The new overall best undergoes also mutation, where its components are perturbed with random numbers following a Cauchy distribution [151].”
- A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, methodology): “[74] presented an enhanced PSO algorithm called GOPSO, which employed generalized OBL (GOBL) and Cauchy mutation.”
- Particle swarm optimization algorithm: an overview (2017, methodology): “…operators to the PSO algorithm, such as selection (Angeline 1998a,b; Lovbjerg et al. 2001), crossover (Angeline 1998b; Chen et al. 2014), mutation (Tsa-farakis et al. 2013) or Cauchy mutation (Wang et al. 2011) to increase the diversity and improve its ability to escape from the local minima.”
- Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “These problems were utilized in previous studies [34,53].”