An effective use of crowding distance in multiobjective particle swarm optimization

By Carlo R. Raquel, Prospero C. Naval Jr.. Annual Conference on Genetic and Evolutionary Computation, 2005

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Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem (2015, methodology): “There are several methods of controlling the external archive such as Maximin fitness based size control [31], epsilondominance based size control [29], and crowding distance based size control [32].”
Multiobjective robust optimization method for drawbead design in sheet metal forming (2010, methodology): “As an extension to PSO, the multiobjective particle swarm optimization incorporating the mechanism of crowding distance computation (MOPSO-CD) has drawn some attention recently as it exhibits a relatively fast convergence and well-distributed Pareto front compared with other multiobjective optimizat”
Crushing analysis and multiobjective crashworthiness optimization of honeycomb-filled single and bitubular polygonal tubes (2011, methodology): “[47].”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, background): “• Raquel and Naval [51]: As in [60], this approach incorporates the concept of nearest neighbor density estimator for selecting the global best particle and also for deleting particles from the external archive of nondominated solutions.”