Stereotyping: improving particle swarm performance with cluster analysis

By James Kennedy. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000

382 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ce8d6f88b2e281ee2b70b91658b5ff888ab0eb8b

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Citations in the corpus

Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis (2010, methodology): “combines the PSO strategy [18,19] and cluster analysis [20–22] (see Fig.”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “Kennedy [26] suggested to cluster particles in each iteration into subswarms, and have them determine their global best separately.”
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (2009, background): “The objective of this class of multimodal optimization problems is to find multiple optima having either equal or unequal function values (Brits et al. 2002; Kennedy 2000; Parsopoulos and Vrahatis 2004).”
Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem (2004, methodology): “This method is a combination of the (unique) queen method defined in [9] and the multiclustering method described in [17].”
Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, methodology): “In order to investigate in detail 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; Kadirkaman”
Two improved differential evolution schemes for faster global search (2005, methodology): “For Schaffer’s f6, the widely used error limit of 0.00001 [ 6 ], [11] was used.”
Orthogonal Learning Particle Swarm Optimization (2009, background): “The cluster centers are calculated in [40] to replace the personal best position or its neighbor’s best position, or both.”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “he computation of FER, however it can reliably locate all global optima when population sizes are large. Clustering techniques such as k-means have been incorporated into the PSO framework by Kennedy [177] as well as by Passaro and Starita [178] who used the Bayesian Information Criterion (BIC) [179”
Particle swarm optimization algorithm: an overview (2017, methodology): “Based on K -means clustering algorithm, Kennedy (2000) also proposed another version of local PSO algorithm, called social convergence method, with the hybrid space neighborhood and ring topology.”
A review of particle swarm optimization. Part I: background and development (2007, background): “A choice need not be made between constriction and inertia; Eberhart and Shi (2000) showed that with judicious parameter settings (x set to v and u 1 + u 2 > 4) the two approaches were algebraically equivalent and improved performance could be achieved across a wide range of problems.”
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004, methodology): “000 01 was used for this investigation [5], [20].”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “CA techniques are also used in PSO to predict a promising leader for guiding the flying behavior of particles [49] and used in DE to generate promising new population according to the cluster centers [50].”
Cooperative learning in neural networks using particle swarm optimizers (2000, methodology): “Kennedy [7] used cluster analysis to modify the update equations so that particles attempt to conform to the center of their clusters rather than attempting to conform t o a global best.”
An analysis of particle swarm optimizers (2002, background): “Kennedy proposed a version of the lbest PSO that is a mixture of the spatial neighbourhood and the ring-topology approaches, called social stereotyping [69].”
Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization (2004, methodology): “To some extent, it provides a way of addressing the three limitations we identified with the clustering approach used in the PSO proposed by Kennedy [5].”