A Cooperative approach to particle swarm optimization

By Frans van den Bergh, Andries Petrus Engelbrecht. IEEE Transactions on Evolutionary Computation, 2004

2,130 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4775958f35124ef72e5c1f2b086cf398af8cb05a

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In Artificial Intelligence

Citations in the corpus

Novel composition test functions for numerical global optimization (2005, methodology): “Algorithms used and associate parameter settings: Particle Swarm Optimizor (PSO) [7]: population size=20 Cooperative PSO (CPSO) [3]: population size=10, number of groups=D=10 Comprehensive Learning PSO (CLPSO) [8]: population size=10, Pc=0.”
A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments (2010, background): “This problem is called “two step forward, one step back” in [40].”
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.”
Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “All benchmark functions except f6 have the global minimum at the origin or very near to the origin [28].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “Functions CPSO-H [1] CLPSO [34] APSO [59] GOPSO [53] DNSCLPSO DNSPSO”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, positive): “By using CL learning strategy and its velocity update equation [14], the particles can learn from different dimensions of different particles and avoid two problems, “ oscillation phenomenon “ [28] and “ two steps forward, one step back phenomenon “ [25].”
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).”
A hybrid model for high-frequency stock market forecasting (2015, methodology): “In this context, hybrid forecasting models, composed of evolutionary algorithms (EAs) (Hansen, 2006; Leung, Lam, Ling, & Tam, 2003; Vandenbergh & Engelbrecht, 2004) and ANNs or MNNs have been proposed in the literature (Asadi, Hadavandi, Mehmanpazir, & Nakhostin, 2012; de A. Araújo, 2007, 2011b,…”
Swarm Intelligence in Optimization (2008, methodology): “A cooperative PSO, similar to those previously developed coevolutionary algorithms, was also proposed in [171].”
Adaptive Particle Swarm Optimization (2008, methodology): “Cooperative approach [40], selforganizing hierarchical technique [18], deflection, stretching, and repulsion techniques [41] have also been hybridized with traditional PSO to enhance performance.”
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, background): “Van den Bergh and Engelbrecht [21] were the first to apply particle swarm optimization (PSO) [22] to a cooperative co-evolutionary framework (CPSO).”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “A number of significantly improved variants of basic PSO have been proposed in the recent past to solve both benchmark and real-world optimization problems, for example, see [38], [39].”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, methodology): “Index Terms— Dynamic environments, genetic algorithms, optimization methods, particle swarm optimization (PSO).”