A hierarchical particle swarm optimizer and its adaptive variant

By Stefan Janson, Martin Middendorf. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2005

370 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/28a0a79a00caef181ab6137424cd0d92ec1735d6

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Citations in the corpus

Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, background): “…in depth by many other researchers in order to ﬁnd other topologies to improve the performance of PSO (Suganthan, 1999; Kennedy, 1999, 2002; Mendes et al., 2004; van den Bergh and Engelbrecht, 2004; Janson and Middendorf, 2005; Liang and Suganthan, 2005; Mohais et al., 2005; Parrott and Li, 2006).”
Swarm Intelligence in Optimization (2008, methodology): “In [83], an H-PSO (Hierarchical PSO) was proposed, where a hierarchical tree structure is adopted to restrict the interactions among particles.”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “φ < 12(ω2−1) 5ω−7 where φ = φ1 = φ2, that is, the same as what was found by Jiang et al. (2007a). Also, Poli (2009) proved that the variance of positions converges to h(φ1, φ2, ω)|g − p| where h(.”
Self regulating particle swarm optimization algorithm (2015, methodology): “Algorithms based on neighborhood topology: Initially, different topologies like fully connected, wheel and Von Neumann [29, 32] were studied and later other topologies were introduced [6, 27, 51, 73] aiming towards enhancing the exploration capabilities of the PSO algorithm.”
Evolutionary dynamic optimization: A survey of the state of the art (2012, background): “Dynamical GA[90], Sentinel Placement [13], Population-Based Incremental Learn627 ing [91], several Particle Swarm Optimisation (PSO) variants [92, 93, 94, 95], and 628 dynamic Evolutionary Multiobjective optimisation [96, 97, 98].”
Genetic Learning Particle Swarm Optimization (2016, background): “These variants can be generally divided into four categories that focus on: 1) population topology and multiswarm techniques [8]–[10]; 2) parameter control [11]–[14]; 3) hybrid methods [15]–[25]; and 4) novel learning schemes [26]–[30], respectively.”
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm (2009, neutral): “Since AHPSO does not use a fixed topology, its RLDs are the same across topologies and its results can therefore be used as a reference across plots for a same problem.”