A Self-Learning Particle Swarm Optimizer for Global Optimization Problems

By Changhe Li, Shengxiang Yang, Trung-Thanh Nguyen. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012

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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”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “Li and Yang [31,32] presented an adaptive learning PSO for function optimization, in which the learning mechanism of each particle is separated into three components: its own historical best position, the closest neighbor and the global best one.”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “A very similar idea was also presented by Changhe et al. (2012) where the method was called “Self-Learning Particle Swarm Optimization,” SLPSO.”
Particle swarm optimization algorithm: an overview (2017, background): “Li et al. (2012) presented a self-learning PSO in which a velocity update scheme could be automatically modiﬁed in the evolution procedure.”
Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, methodology): “Additionally, Li et al. (2012) introduced an adaptive framework into PSO to develop a self-learning PSO (SLPSO) algorithm.”