Multiswarms, exclusion, and anti-convergence in dynamic environments

By Tim M. Blackwell, J. Branke. IEEE Transactions on Evolutionary Computation, 2006

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A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “Both algorithms were improved by re-initializing the worst performing swarm whenever all swarms converge for maintaining diversity [164].”
A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, methodology): “Traditionally, the search area of each subregion is predefined by users according to preliminary experiments [30] or a formulated estimation [ 5 ], and the search area for all subregions is the same.”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, background): “These make offline datadriven EAs more challenging than online data-driven EAs, particularly when the data is imbalanced [41]–[43], noisy [44], time-varying [45], or heterogeneous [46].”
An improved particle swarm optimization for feature selection (2011, background): “Blackwell and Branke[7] split the population of particles into a set of interacting swarms.”
Swarm Intelligence in Optimization (2008, background): “Several studies have suggested various approaches to applying PSO to solve dynamic optimization problems [28, 29, 56, 82, 103, 11, 12, 135].”