A Diversity-Guided Particle Swarm Optimizer - the ARPSO

By J. Riget, J. Vesterstrom. 2002

378 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e91e20f33c37bc370fd266172440cd508688ad63

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Citations in the corpus

A hierarchical particle swarm optimizer and its adaptive variant (2005, methodology): “The ARPSO algorithm [20] switches between phases of attraction and repulsion.”
A review of particle swarm optimization. Part I: background and development (2007, background): “Riget and Vesterstrøm (2002) implemented a measure of diversity to control the alternating attraction and repulsion of the particles to and from the swarm best position.”
Measuring exploration/exploitation in particle swarms using swarm diversity (2008, background): “The diameter of a swarm is defined as the maximum distance between any 2 particles in the swarm [2] and the radius can be calculated as the distance between the swarm center and the particle in the swarm which is furthest away from it.”
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems (2004, methodology): “The attractive and repulsive PSO (arPSO) [9], [15] was introduced by Vesterstrøm and Riget to overcome the problems of premature convergence [1].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “In the repulsive phase, the subtraction operator is employed (Riget and Vesterstroem 2002).”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “Riget [42] proposed a diversity-guided PSO, called ARPSO, which defines a repulsion phase based on a modified velocity updating model.”
Swarm Intelligence in Optimization (2008, methodology): “For example, ARPSO (attractive and repulsive PSO) was proposed to use a diversity measure to trigger an alternation between phases of attraction and repulsion [148].”
Particle swarm optimization algorithm: an overview (2017, methodology): “Other methods or models to improve the diversity included: attracting-exclusion model (Riget and Vesterstrom 2002), predator-prey model (Gosciniak 2015), uncorrela-tive component analysis model (Fan et al. 2009), dissipative model (Xie et al. 2002), self-organizing model (Xie et al. 2004), life…”
Simplifying Particle Swarm Optimization (2010, methodology): “[9] [10]), and combining different optimization methods into a hybrid one (see e.”