A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments

By Shengxiang Yang, Changhe Li. IEEE Transactions on Evolutionary Computation, 2010

350 citations recorded by the source corpus.

How researchers see this paper

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

Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, background): “Due to the close relation with diversity maintenance, density estimation is very important in EAs and is widely applied in various optimization scenarios, such as multimodal optimization [62], dynamic optimization [76], and robustness optimization [45].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “The clustering PSO (CPSO) algorithm proposed by Li and Yang [149, 99] uses a hierarchical clustering method to generate sub-swarms whenever a change is detected.”
Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, methodology): “Yang and Li [31] used hierarchical clustering technique to divide the population into different subpopulations, and the main advantage of this design is that the initial indi-”
A Multi-Facet Survey on Memetic Computation (2011, background): “When dealing with problems with uncertainties, population diversity is enhanced by locating and tracking multiple moving peaks via hierarchical clustering [133] and variable relocation evolutionary process [134] which makes already converged individuals evolve more rapidly to new environmental condi”
Evolutionary dynamic optimization: A survey of the state of the art (2012, background): “Another recent PSO example that also has multi swarms with 1007 equal roles is the Clustering PSO in [145, 146].”