Dynamic multi-swarm particle swarm optimizer

By Jing J. Liang, Ponnuthurai Nagaratnam Suganthan. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005

536 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e8fc8f1fbc191cbb38735a80f8f8e878c424be7b

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Citations in the corpus

A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “In the experiments on low-dimensional optimization problems, five representative PSO variants have been chosen to compare with the proposed SL-PSO, including the global version PSO (GPSO) [73], the local version PSO (LPSO) [44], the fully informed PSO (FIPS) [53], the dynamic multi-swarm PSO (DMS-PS”
A review of population-based meta-heuristic algorithm (2013, methodology): “Also, Dynamic Multi-Swarm PSO (DMS-PSO) introduced by Liang and Suganthan [66] to improve the topological structure in dynamic way.”
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.”
Dynamic multi-swarm particle swarm optimizer with local search (2005, methodology): “In [10], we proposed a dynamic multi-swarm particle swarm optimizer (DMS-PSO) whose neighborhood topology is dynamic and randomly assigned.”
Self regulating particle swarm optimization algorithm (2015, methodology): “From the category of neighborhood topology, the dynamic multi-swarm particle swarm optimizer [37] and fully informed particle swarm [45] have been selected.”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “D functions, the number of swarms varied from 4 to 50, creating an average size around 240; in DMS-PSO [30], a larger swarm size 450 was adopted for the optimization of 500-”
Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions (2019, background): “, dynamic clustering [19], [20], supervised learning [21], historical memory [22], and adopting different learning strategies [23], [24].”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “The sixth is a dynamic multi-swarm PSO (DMS-PSO) [37] which is designed to improve the topological structure in a dynamic way.”
Adaptive Particle Swarm Optimization (2008, background): “12(c) shows that while optimizing the function f 7 , we have the following: 1) the APSO, the CLPSO, and the HPSO-TVAC manage to obtain acceptable solutions in all the trials, but the APSO is faster than the CLPSO and the HPSO-TVAC; 2) only about two-thirds of the trails in the GPSO and the FIPS obta”
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).”
Particle swarm optimization algorithm: an overview (2017, methodology): “Dynamic multi-swarm PSO was proposed by Liang and Suganthan (2005) where the swarm was divided into several sub-swarm, and these sub-swarms were regrouped frequently to share information among them.”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “The concepts of “multi-swarm” and “multi-population island models” have been introduced to improve the performance of particle swarm optimization (PSO) and DE in several studies [4,28,29,35,64,68,70].”