Self-adaptive learning based particle swarm optimization

By Yu Wang, Bin Li, Thomas Weise, Jianyu Wang, Bo Yuan, Qiongjie Tian. Information Sciences, 2011

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/03fa64ccd6858081fb4e5ec11cb52dfec0d4be44

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In Artificial Intelligence

In Computational Theory and Mathematics

In Electrical and Electronic Engineering

Citations in the corpus

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “For example, a self-adaptive method was proposed in which the most effective PSO variant was selected during the run for the problem at hand (Wang et al., 2011).”
Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud (2014, methodology): “Based on the work in [23], in this paper a self-adaptive learning PSO-based scheduling approach is proposed to solve the DCTS problem.”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “By means of a similar logic, in [222] a memetic swarm intelligence approach is used for multimodal optimization.”
A swarm optimization algorithm inspired in the behavior of the social-spider (2013, background): “A detailed description of each function is given in the Appendix A.”
Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, methodology): “Furthermore, Wang et al. (2011) proposed a self-adaptive learning based PSO (SLPSO) with a new probability model that was used ACM Transactions on Knowledge Discovery from Data, Vol. 13, No. 5, Article 50.”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “Wang et al. [56] introduced a self-adaptive learning strategy to improve the performance of CLPSO.”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “The poor performance of PSO can largely be attributed to its weak robustness to various problem structures [84].”
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, background): “Tables 14 and 15 show that RABC performed better than other algorithms except for CLPSO.”