Bare bones particle swarms

By James Kennedy. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2003

762 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5fbaacea81b1562d2111ae1e3c795b1643c86f54

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Citations in the corpus

Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, methodology): “…probability distributions to generate random numbers to updating the velocity equation of PSO have been proposed (Coelho & Alotto, 2008; Coelho & Herrera, 2007; Coelho & Lee, 2008; Higashi & Iba, 2003; Kennedy, 2003; Krohling, 2004; Krohling & Coelho, 2006a, 2006b; Secrest & Lamont, 2003).”
A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012, methodology): “Kennedy [19] proposed also an alternative version of the BBPSO, denoted by BBExp, where (9) is replaced by”
Water wave optimization: A new nature-inspired metaheuristic (2015, background): “Such a calculation has turned out to be competitive for many difficult numerical optimization problems [37].”
Particle swarm optimization algorithm: an overview (2017, methodology): “Bare Bones PSO (BBPSO) was proposed by Kennedy (2003) as a model of PSO dynamics.”
A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “The earliest model-based PSO was proposed by Kennedy [42], called the Bare Bones PSO (BBPSO), which uses Gaussian distributions instead of the velocity equation to update the particle positions.”
Self regulating particle swarm optimization algorithm (2015, methodology): “The PSO algorithm was investigated by eliminating some of its traditional features in [30] referred to as a Bare Bones Particle Swarm Optimization (BBPSO).”
Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems (2006, methodology): “Kennedy [19] also used a Gaussian distribution in his bare bones particle.”
Swarm Intelligence in Optimization (2008, background): “Kennedy proposed a PSO variant, bare-bones PSO, which does not use the velocity term [89].”
A review of particle swarm optimization. Part I: background and development (2007, background): “Kennedy (2003) revisited the constricted PSO and examined whether the added components were necessary, and whether any further components could be removed.”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, background): “It has been argued that the PSO metaphor is more important than the implementation itself (Kennedy 2003).”
Particle Swarm Methods (2018, methodology): “The Bare Bones PSO (BBPSO) was introduced in 2003 [58] as a simplification of the Canonical PSO.”
DEPSO: hybrid particle swarm with differential evolution operator (2003, background): “Keywords: Particle swarm optimization, differential evolution, numerical optimization.”
Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, methodology): “…and Kennedy, 2002), the standard PSO (Bratton and Kennedy, 2007), Gaussian PSO (Secrest and Lamont, 2003), Gaussian bare-bones PSO (Gaussian BBPSO; Kennedy, 2003, 2004), PSO with the exponential distribution (PSO-E; Krohling and Coelho, 2006), L´evy PSO (Richer and Blackwell, 2006), and QPSO were…”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, neutral): “In static heterogeneous particle swarm optimizer (sHPSO), different velocity updating rules of PSO [3], cognitive-only velocity update [33], social-only velocity update [33], bare-bones PSO [34], modi ﬁ ed barebones PSO [34] were used.”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “Note that PSO variants that belong to the family of Bare-Bones PSO (Blackwell, 2012; Kennedy, 2003) are not reviewed in this study as this topic needs”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, methodology): “Zhang et al. [31] analyzed the sampling distribution in BBPSO, based on which they propose an adaptive version inspired by the cloud model, which adaptively produced a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which prov”