Particle swarm optimisation with spatial particle extension

By Thiemo Krink, J. Vesterstrom, J. Riget. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, background): “The main goal is to increase the diversity of the population by: 1) either preventing the particles to move too close to each other and collide [104], [105] or 2) to self-adapt parameters such as the constriction factor, acceleration con-”
Measuring exploration/exploitation in particle swarms using swarm diversity (2008, background): “Several measures of diversity can be found in literature, with different definitions given in [2], [3], [4], [5], [6], [7].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, background): “When particles move in their species, a parallel multi-objective optimization takes place and helps in increasing convergence (Krink et al. 2002).”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “There have been some other modifications proposed for update rules of SPSO (Krink et al., 2002; Chang-Huang and Jia-Shing, 2009; Kaucic, 2013).”