Particle Swarm Optimization With an Aging Leader and Challengers

By Wei-neng Chen, Jun Zhang, Ying Lin, Ni Chen, Zhi-hui Zhan, Henry Shu-Hung Chung, Yun Li, Yu-hui Shi. IEEE Transactions on Evolutionary Computation, 2013

589 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8a14cd4d89d3b9431bde792d9d826d7c2c407383

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Citations in the corpus

A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “The proposed SL-PSO has been tested on 47 functions, including 12 widely used basic test functions [91,4,97,96,8] (f 1 to f 12, refer to Table 1), 28 functions taken from the whole test suite for CEC’13 special session on real-parameter optimization (f 13 to f 40, refer to [47]), and 7 functions tak”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “…of PSO variants have been proposed, including the parameter adaptation based variants (Zhan et al. 2009; Hu et al. 2013), the new topological structure based variants (Liang et al. 2006; Cheng and Jin 2014), and the hybridization based variants (Chen et al. 2013; Wang et al. 2013), to name a few.”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, background): “[60] integrate an aging strategy into the particle evolution for addressing the problem of premature convergence.”
A social spider algorithm for global optimization (2015, methodology): “• Deterministic parameter schemes use some pre-defined rules to change the parameter values throughout the search [29][31].”
Particle swarm optimization algorithm: an overview (2017, background): “ALC-PSO (Chen et al. 2013) endowed the swarm leader an increasing age and a lifespan to escape from the local optima and thus avoid the premature convergence.”
Self regulating particle swarm optimization algorithm (2015, methodology): “This list comprises of teaching and peer learning PSO [40], bidirectional teaching and peer-learning PSO [39], adaptive two-layer PSO with elitist learning strategy [38], orthogonal learning strategy for PSO [82], adaptive learning PSO [34], ageing mechanism transformed PSO [10], distance based loca”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “In addition, various search operators based on sociological or biological concepts have also been proposed, such as the niching PSO [32], cultural-based PSO [33] and the aging theory inspired PSO (ALC-PSO) [13].”