A new locally convergent particle swarm optimiser

By Frans van den Bergh, Andries Petrus Engelbrecht. IEEE International Conference on Systems, Man and Cybernetics, 2002

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/02589400954875beeec5f6036dd5697964e24e75

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Citations in the corpus

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “In 2009, the lack of EFHT (runtime) analysis for PSO was pointed out by Witt (2009). The author argued that most convergence analyses for SPSO (including Clerc and Kennedy, 2002, Jiang et al.”
Particle swarm optimization algorithm: an overview (2017, methodology): “…of social animals with the artiﬁ-cial life theory, for how to construct the swarm artiﬁcial life systems with cooperative behavior by computer, Millonas proposed ﬁve basic principles (van den Bergh 2001): (1) Proximity: the swarm should be able to carry out simple space and time computations.”
Particle Swarm Methods (2018, methodology): “0/ D 1, Tsuc D 15, and Tfail D 5, were recommended in [145], where GCPSO was shown to be very promising on typical benchmark problems.”
Dynamic clustering using particle swarm optimization with application in image segmentation (2006, background): “[62] for a study of the relationship between the inertia weight and acceleration constants, in order to select values which will ensure a convergent behavior.”
A review of particle swarm optimization. Part I: background and development (2007, methodology): “To avoid this, van den Bergh and Engelbrecht (2002) introduced the Guaranteed Convergence PSO (GCPSO), which uses a different velocity update equation for the pg particle. This equation causes the particle to perform a random search around pg within a radius defined by a scaling factor that adapts t”
Particle swarm optimization method for image clustering (2005, methodology): “In GCPSO, the global best particle with index τ is updated using a different velocity update, namely v τ,j (t + 1) = −x τ,j (t) + ˆ y j (t) + wv τ,j (t) + ρ(t)(1 − 2r 2,j (t)) (8) which results in a position update of x τ,j (t + 1) = ˆ y j (t) + wv τ,j (t) + ρ(t)(1 − 2r 2,j (t)) (9) The term −x τ 'r”