The fully informed particle swarm: simpler, maybe better

By Rui Mendes, James Kennedy, José Neves. IEEE Transactions on Evolutionary Computation, 2004

1,759 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7f9e5228984d08d4c00f3ab1bea190cbb06d752b

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In Artificial Intelligence

Citations in the corpus

Dynamic multi-swarm particle swarm optimizer with local search (2005, background): “Focusing on improving the local version of PSO, different neighborhood structures are proposed and discussed [3][4][5][6][7].”
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”
Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, methodology): “Topologies-based PSO [47] Fully informed particle swarm [48] Parallel DE [51] Distributed DE [52], [53] Global and local neighborhoods DE [49], [50] DE with proximity-based mutation operators [54] Ring topology PSO [28] Crowding DE [6] Sharing DE [6] Species-based DE [13] Species-based PSO [28] Fitn”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “[37] proposed a fully informed PSO algorithm (FIPS), in which the neighbors of each particle, instead of pbest and gbest, are used to update the velocity.”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “The fourth is a “fully informed” PSO (FIPS) [39] that uses all the neighbors to influence the flying velocity.”
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm (2009, neutral): “[15] proposed the fully informed particle swarm (FIPS), in which a particle uses information from all its topological neighbors.”
Enhancing particle swarm optimization using generalized opposition-based learning (2011, methodology): “Results of PSO-w, PSO-cf, UPSO, FDR-PSO, FIPS, CPSO-H and CLPSO are taken from Table 4 in [19].”
Stability analysis of the particle dynamics in particle swarm optimizer (2006, methodology): “Empirical evidence has been accumulated to show that the algorithm is a useful tool for optimization [3], [4].”
Particle Swarm Methods (2018, methodology): “A multitude of neighborhood structures (more than 1000) were tested in [80] and interesting conclusions were derived regarding the potential of FIPS to improve the Canonical PSO.”
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2013, methodology): “Additionally, OCABC is compared with the standard PSO, the “fully informed” PSO (FIPS) [49], the self-organizing hierarchical PSO with time-varying acceleration coefficients (HPSOTVAC) [50], the comprehensive learning PSO (CLPSO) [51], and orthogonal learning PSO (OLPSO-G) [32] in Table XIV.”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “Many different topologies have been defined for PSO to date (Mendes et al., 2004), for example, global best topology, ring topology, wheel topology, pyramid typology; each of these have some advantages and disadvantages (Clerc, 2006; Mendes et al.”
Optimal placement of multi-distributed generation units including different load models using particle swarm optimization (2011, methodology): “In [42,43], the fully informed particle swarm optimization algorithm (FIPS) was proposed.”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, neutral): “Based on the ﬁ ndings in [8,10], a fully-informed PSO (FIPS) was proposed in which information from fully connected neighborhood was used [11] and different neighborhood topologies were studied in [12].”
Theory and Use of the EM Algorithm (2011, background): “286 EM Variants global optimizer (particle swarm optimization [38]), and 50 times more accurate for the same number of guesses than random guessing.”