An analysis of particle swarm optimizers

By Frans van den Bergh, Andries Petrus Engelbrecht. 2002

1,542 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ec2c4cd01925c9b015a6115d9a16d841a0f598c1

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Citations in the corpus

A particle swarm pattern search method for bound constrained global optimization (2007, methodology): “Keywords Direct search · Pattern search · Particle swarm · Derivative free optimization · Global optimization · Bound constrained nonlinear optimization AMS subject classifications 90C26 · 90C30 · 90C56 Support for A. Ismael F. Vaz was provided by Algoritmi Research Center, and by FCT under grants…”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, unknown): “These values are chosen based on the common settings in the literature [8], [49].”
Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, background): “…the mechanism of PSO, a lot of theoretical analyses have been done on the algorithm (Kennedy, 1998; Ozcan and Mohan, 1999; Clerc and Kennedy, 2002; van den Bergh, 2002; Shi and Eberhart, 1998b; Trelea, 2003; Emara and Fattah, 2004; Gavi and Passino, 2003; Kadirkamanathan et al., 2006; Jiang et…”
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”
A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, methodology): “In [10], a niching PSO (NichePSO) was proposed by incorporating a cognitive only PSO model and the guaranteed convergence PSO algorithm [39].”
Simplifying Particle Swarm Optimization (2010, methodology): “Several variants of the PSO method are developed by van den Bergh [3] and tested on different kinds of ANNs.”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, unknown): “4 (as suggested in [31]) (for TV-MOPSO).”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “e removing all local optima violating the threshold constraint. However, the effectiveness of the stretching method is not uniform on all objective functions and introduced false minima in some cases [33]. This approach was improved by the introduction of the Deflection and Repulsion techniques in [”
A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (2005, methodology): “Other schemes rely on limiting the magnitude of the velocities, either explicitly [16] or by modifying the constriction factor χ and the other ‘constants’ w, c2 and c2 appearing in the update equations [17].”
Swarm Intelligence in Optimization (2008, methodology): “Since PSO was first introduced by Kennedy and Eberhart in 1995 [91], several studies have been carried out on understanding the convergence properties of PSO [33, 132, 170, 168].”
Particle swarm optimization algorithm: an overview (2017, background): “…it could only converge to the optima that the swarm could search, and could not guarantee that the achieved solution was the best, even it could not guarantee that it was the local optima. van den Bergh and Engelbrecht (2002) proposed a PSO algorithm which could ensure the algorithm convergence.”