Particle swarm optimization: developments, applications and resources

By R. Eberhart, Yuhui Shi. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001

4,448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/6ba524fef495a5d20e638038194bcfef6dd62270

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Citations in the corpus

Metaheuristics: review and application (2013, methodology): “A review of developments in PSO and their application is given in the study by Eberhart and Shi (2001).”
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, methodology): “For this reason, the success of the DS algorithm in solving numerical optimization problems is compared with those of the ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011 and CMA-ES algorithms.”
A Hybrid Approach Based on PSO and Hadamard Difference Sets for the Synthesis of Square Thinned Arrays (2009, background): “Moreover, and are random positive coefficients drawn from a uniform distribution with predefined upper limits [ 20 ].”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “Another "best" value that is tracked by the particle swarm optimizer is the overall best value, and its location, obtained by any particle in the population [57], [58] – the so-called “global best”.”
Dissipative particle swarm optimization (2002, methodology): “ed the performance of the PSO with different settings, e.g., neighborhood settings [6], cluster analysis [7], etc. It has been used for approaches that can be used across a wide range of applications [8]. However, studies by Angeline [9] showed that although PSO discovered reasonable quality solutio”
Particle swarm optimization versus genetic algorithms for phased array synthesis (2004, background): “” Following common practice in the literature [25], , although these values could be tuned for the problem at hand.”
Design of a Single-Feed Dual-Band Dual-Polarized Printed Microstrip Antenna Using a Boolean Particle Swarm Optimization (2008, background): “More details on how to determine the ideal parameters for PSO implementation can be found in [34], [35].”
Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “So considering these factors, seven optimization algorithms have been chosen namely, ABC (Karaboga and Basturk 2007), CS (Yang and Deb 2009), DE (Storn and Price 1997), FA (Yang 2010b), GA (Goldberg and Holland 1988), MBO (Wang et al. 2015) and PSO (Eberhart and Shi 2001).”
Boundary Conditions in Particle Swarm Optimization Revisited (2007, methodology): “A constriction factor or an explicit maximum velocity [3] is usually utilized in PSO algorithms to control the exploration abilities of particles; however, it cannot prevent them from going outside the allowable solution space and hence producing invalid solutions.”
Computational approach based on a particle swarm optimizer for microwave imaging of two-dimensional dielectric scatterers (2005, methodology): “To reduce excessively large step sizes in the particle's fly, clamp to a specified maximum value (according to the reference literature [19], [25], [26], is set to the dynamic range of the th dimension).”
Particle swarm optimization algorithm: an overview (2017, methodology): “An extension of this work adopted fuzzy systems to nonlinearly change the inertia weight during optimization (Shi and Eberhart 2001).”
Adaptive Particle Swarm Optimization (2008, methodology): “Meanwhile, much research on performance improvements has been reported, including parameter studies, combination with auxiliary operations, and topological structures [4], [5], [10].”
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis (2014, methodology): “Later, Shi [36] introduced inertia weight into the Particle Swarm Optimizer to produce the standard PSO [36,37].”
Design of a fractional order PID controller for an AVR using particle swarm optimization (2009, positive): “Also c1 and c2 are the acceleration constants which influence the convergence speed of each particle and are often set to 2.0 according to the past experiences (Eberhart & Shi, 2001).”