Adaptive particle swarm optimization: detection and response to dynamic systems

By Xiaohui Hu, R. Eberhart. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

393 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/de7a3d2e30eff2599259635a3ebb9f064bbc7676

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In Artificial Intelligence

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Yaochu Jin
4. Shengxiang Yang
5. Changhe Li
6. Seyedali Mirjalili
7. Xin-She Yang
8. Seyed Mohammad Mirjalili

Citations in the corpus

• Particle swarm optimization algorithm: an overview (2017, background): “Hu and Eberhart (2002) studied a dynamic neighborhood, and in each generation, the nearest m particles were selected to be the new neighbors of one particle.”
• Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “certain particles, rerandomizing the or a combination of the previous strategies [144], [145].”
• Stability analysis of the particle dynamics in particle swarm optimizer (2006, background): “On the algorithmic front, extensions have been made to deal with dynamical environments and efficient exploration [11], [12].”
• Multi-swarm Optimization in Dynamic Environments (2004, background): “Hu and Eberhart [15] list a number of these, which all involve randomization of the entire, or part of, the swarm.”
• Adaptive Particle Swarm Optimization (2008, background): “In addition, departing from mutation [23], reset [25], [26], or reinitialization [27], [28] operations, the ELS is pro-”
• 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.”
• Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, methodology): “Hu and Eberhart [23] list a number of these which all involve randomization of the entire, or part of, the swarm.”
• Particle swarm optimization versus genetic algorithms for phased array synthesis (2004, background): “Some researchers find advantage in allowing this multiplier to take random values [17], decrease during the optimization process to encourage local searching at the end of the optimization process [18]–[22], randomly vary during the optimization process [23], or be set to zero altogether [24].”
• Bio-inspired computation: Where we stand and what's next (2019, background): “dom restart strategies [78,79], the so-called hyper-mutation operator [80], which imprints an increase of the mutation rate once a change has been detected; or the migration of individuals among subpopulations in multi-population schemes for dynamic optimization, a strategy to actively diversify the”