Comparing inertia weights and constriction factors in particle swarm optimization

By R. Eberhart, Yuhui Shi. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000

3,199 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/d5af1b957ae11fcd806551fef2ba0e31ca3008cf

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Citations in the corpus

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “stricting the velocities is equivalent in significance to the inertia weight variation, given its impact on determining solution quality across neighborhoods in the search space. Empirical studies in [21] demonstrated that faster convergence rates are achieved when velocity constriction is used in c”
Evolutionary optimization as applied to inverse scattering problems (2009, background): “Concerning the optimal choice of the control coefficients, it is still worthwhile pointing out that since higher values of ω produce relatively straight particle trajectories, resulting in a good global search characteristic, while small values of ω encourage a local searching, some researchers have”
Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “This was called PSO-TVIW method (PSO with Time varying inertia weight) [15].”
Document clustering using particle swarm optimization (2005, methodology): “It was successfully applied in many research and application areas [4, 7, 13].”
Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch (2008, methodology): “Eberhart and Shi [25] observed that population size does not affect the performance of a PSO algorithm significantly.”
Particle swarm optimization algorithm: an overview (2017, methodology): “However, even the constriction factor was used, experiments showed that better result would be obtained if the particle velocity was limited simultaneously (Eberhart and Shi 2000), so the idea of speed limitation was still retained in PSO algo-rithm.”
Two improved differential evolution schemes for faster global search (2005, background): “Through empirical studies on numerical benchmarks, Eberhart and Shi [4] suggested that it is good to limit the maximum velocity Vmax to the upper limit of the dynamic range of search, i.e., Xmax.”
A review of particle swarm optimization. Part I: background and development (2007, background): “A choice need not be made between constriction and inertia; Eberhart and Shi (2000) showed that with judicious parameter settings (x set to v and u 1 + u 2 > 4) the two approaches were algebraically equivalent and improved performance could be achieved across a wide range of problems.”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, background): “The inertia constant can be either implemented as a fixed value or can be dynamically changing [86], [89], [90], [93], [97].”