Measuring exploration/exploitation in particle swarms using swarm diversity

By O. Olorunda, Andries Petrus Engelbrecht. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

300 citations recorded by the source corpus.

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Grey Wolf Optimizer (2014, background): “Regardless of the differences between the meta-heuristics, a common feature is the division of the search process into two phases: exploration and exploitation [8-12].”
Henry gas solubility optimization: A novel physics-based algorithm (2019, unknown): “The exploration phase generates new solutions over the entire search space to avoid local optima [9, 10].”
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (2018, background): “The parameter f is varied from [1, 2] to [4, 5].”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, unknown): “(10) and (11) [30,31].”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “In order to obtain measurabl e observations, adiversity measureintroduced in [62], [63] is adopted here to indicate the change of diversity during the search process:”
A novel algorithm for global optimization: Rat Swarm Optimizer (2020, background): “However, every optimization algorithm needs to address and maintains a good balance between the exploration and exploitation phases of a search space (Alba and Dorronsoro 2005; Olorunda and Engelbrecht 2008).”
Slime mould algorithm: A new method for stochastic optimization (2020, unknown): “Although different MAs have some distinctness, they all have two identical stages in the search gradation: exploration and exploitation [27, 28].”