Tracking and optimizing dynamic systems with particle swarms

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

1,080 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/84106185b13a58a07969f1bcf5a2c1df7f3a74b5

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Citations in the corpus

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004, methodology): “Mutation is widely used in most evolutionary optimization methods, such as evolutionary programming and genetic algorithms, to guide and enhance the search toward the global best solution [8], [13], [31], [35].”
A review of population-based meta-heuristic algorithm (2013, methodology): “A fuzzy adaptive w was introduced and a random version setting w to was experimented for dynamic system optimization [68, 69].”
Particle swarm optimization: developments, applications and resources (2001, methodology): “It has been demonstrated that particle swarm optimization can be successfully applied to tracking and optimizing dynamic systems (Eberhart and Shi 2001a).”
A review of particle swarm optimization. Part I: background and development (2007, methodology): “Eberhart and Shi (2001) used a standard PSO that had a randomly varying inertia value with a mean of 0.75 and range of 0.5‐1.0.”
An analysis of particle swarm optimizers (2002, methodology): “This question is partially answered in a paper by Eberhart and Shi, also studying the ability of the PSO to track a dynamic minimum [36].”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “The random diagonal matrices were replaced by rotation matrices in all planes (generated by the proposed method) in several PSO variants proposed in (Clerc and Kennedy, 2002; Eberhart and Shi, 2001; Nickabadi et al., 2011; PSO, 2006; Shi and Eberhart, 1998b; Van den Bergh and Engelbrecht, 2010; Zhen”
Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization (2002, methodology): “Parameters settings were used as before [8; 9].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “In [198, 199] a conventional PSO algorithm was applied on the time-varying parabolic function that reflects a simple unimodal fitness landscape.”
Swarm Intelligence in Optimization (2008, methodology): “Traveling salesman problem Dorigo, Maniezzo, and Colorni [46, 50, 51] Dorigo and Gambardella [49] Stützle and Hoos [164] Quadratic assignment problem Maniezzo [109] Maniezzo and Colorni [111] Stützle and Hoos [164] Scheduling problems Stützle [162] den Besten, Stützle, and Dorigo [41] Gagné, Price, ”