Population structure and particle swarm performance

By James Kennedy, Rui Mendes. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

1,674 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/007e2db87ce151210a69abf82ec62b7fd92daa1e

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Citations in the corpus

Particle swarm optimization algorithm: an overview (2017, background): “Kennedy (1999) and Kennedy and Mendes (2002, 2003) had analyzed different kinds of static neighborhood structures and their inﬂuences on the performance of PSO algorithm, and regarded that adaptability of the star, ring and von Neu-manntopologywerebest.”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “Different particle topologies are explored in [24] and [28], but the standard neighborhood is global ( gbest ), i.e., all particles know the position of the globally best solution found so far .”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “Several topological structures have been proposed [22], including the ring topology and the von Neumann topology.”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, methodology): “to replace the traditional global topology [13]–[17], hybridizing PSO with auxiliary search techniques [18]–[34], and using multiswarm techniques [35]–[38].”
A review of population-based meta-heuristic algorithm (2013, background): “In topological structures, Kennedy and Mendes proposed a ring topological structure PSO (LPSO) [64] and a Von Neumann topological structure PSO (VPSO) [65] to enhance the performance in solving multimodal problems.”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “It essentially describes a subset of particles with whom a particle can initiate information exchange [28].”
A review of particle swarm optimization. Part I: background and development (2007, background): “See also, the further work in Mendes et al. (2003). Particle swarms, in common with many population based algorithms, have an unintentional bias to perform better in problems that have optimal solutions toward the certain areas of the swarm’s initialised position, for example the centre of the solut”
A hierarchical particle swarm optimizer and its adaptive variant (2005, background): “In [14] and [24], several neighborhood topologies or " sociometries " have been examined for the PSO algorithm.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, methodology): “A total of 1343 random topologies and six special topologies were tested in (Kennedy and Mendes 2002), including gbest , lbest , pyramid, star, small, and von Neumann.”
A survey on optimization metaheuristics (2013, background): “The effects of various population topologies on the particle swarm algorithm were investigated in [148].”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “convergence to the global best vector while the ring topology favors explorative (over exploitative) behavior and slow convergence to the global best vector (Kennedy and Mendes, 2002).”
Adaptive Particle Swarm Optimization (2008, methodology): “The LPSO with a ring topological structure and the von Neumann topological structure PSO (VPSO) have been proposed by Kennedy and Mendes [45], [46] to enhance the”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, background): “ACO algorithms have an advantage over simulated annealing (SA) and GA approaches when the graph may change dynamically, since the ant colony algorithm can be run continuously and adapt to changes in real time [71], [72].”