Probabilistic convergence analysis of the stochastic particle swarm optimization model without the stagnation assumption

DOI10.1016/J.INS.2020.08.072zbMath1475.68478OpenAlexW3082599879WikidataQ114666906 ScholiaQ114666906MaRDI QIDQ2056351

Yanyan Li

Publication date: 2 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.08.072

zbMATH Keywords

stability analysisparticle swarm optimizationprobabilistic metric spaceprobabilistic convergencestochastic PSO model

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (3)

Analytical stability analysis of the fractional-order particle swarm optimization algorithm ⋮ Convergence analysis of flow direction algorithm in continuous search space and its improvement ⋮ A partition-based convergence framework for population-based optimization algorithms

Cites Work

• Convergence and stochastic stability analysis of particle swarm optimization variants with generic parameter distributions
• Fixed point theorems in probabilistic metric spaces
• Completeness results in probabilistic metric spaces
• Statistical metric spaces
• Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm
• The particle swarm optimization algorithm: Convergence analysis and parameter selection
• A comprehensive survey on particle swarm optimization algorithm and its applications
• Convergence analysis of standard particle swarm optimization algorithm and its improvement
• A study of particle swarm optimization particle trajectories
• Extension of contractive maps in the Menger probabilistic metric space
• A convergence theory for probabilistic metric spaces

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