Particle swarm optimization with particles having quantum behavior

By Jun Sun, Bin Feng, Wenbo Xu. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), 2004

1,392 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/15f584ac30e372455a6d164454679b6e8e29ae16

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. Leandro dos S. Coelho
2. Konstantinos E. Parsopoulos
3. Viviana Cocco Mariani
4. Lei Liu
5. Dongshu Wang
6. Dapei Tan
7. Wenbo Xu
8. Sanchita Basak

Citations in the corpus

• Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “tain degree. There are however, several major shortcomings of the basic PSO that introduce failure modes such as stagnation and convergence to local optima which has led to extensive studies (such as [4, 5]) aimed at mitigation and resolution of the same. In this review, the foundations and frontier”
• COVID-19 Outbreak Prediction with Machine Learning (2020, background): “All members move in perfect harmony with each other, hunt together if they are to be hunted, and escape from the clutches of a predator by moving another prey if they are to be preyed upon [63] [64] [65] .”
• Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, methodology): “This is the motivation of the proposed QPSO algorithm which is described below (Sun, Feng et al., 2004).”
• Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, methodology): “Employing the Monte Carlo method, the particles move according to the following iterative equation (Cai et al., 2008; Liu et al., 2005; Sun et al., 2004; Sun et al., 2005; Xi, Sun, & Xu, 2008):”
• Particle Swarm Methods (2018, methodology): “Quantum PSO (QPSO) was introduced in 2004 [133], putting the algorithm in a new framework.”
• Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects (2008, methodology): “The setup of QPSO and HQPSO approaches used was the following: QPSO: QPSO proposed in [21,22] using a constant contraction– expansion coefficient given by b = 0.”
• Particle swarm optimization algorithm: an overview (2017, methodology): “PSO algorithm was also extended in quantum space by Sun et al. (2004).”
• Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China (2018, methodology): “This notation also applies to the SOFNN, SVR, and PSO algorithms.”