Convergence analysis of particle swarm optimization using stochastic Lyapunov functions and quantifier elimination

Gerwien, Maximilian; Voßwinkel, Rick; Richter, Hendrik

doi:10.48550/arxiv.2002.01673

Cited by 1 publication

(1 citation statement)

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“…CURRENT PSO STABILITY ANALYSIS Almost all existing work has derived stability criteria directly for specific PSO variants. The CPSO algorithm has undergone the most theoretical stability analysis, from the earlier deterministic model works of [4], [19], [20] to the more recent stochastic works of [21], [8], [18], [22], [23]. A number of PSO variants have been directly studied [6], [16], [24], [25].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Particle Swarm Optimization: Stability Analysis using N-Informers under Arbitrary Coefficient Distributions

Cleghorn¹,

Stapelberg²

2020

Preprint

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This paper derives, under minimal modelling assumptions, a simple to use theorem for obtaining both order-1 and order-2 stability criteria for a common class of particle swarm optimization (PSO) variants. Specifically, PSO variants that can be rewritten as a finite sum of stochastically weighted difference vectors between a particle's position and swarm informers are covered by the theorem. Additionally, the use of the derived theorem allows a PSO practitioner to obtain stability criteria that contains no artificial restriction on the relationship between control coefficients. Almost all previous PSO stability results have provided stability criteria under the restriction that the social and cognitive control coefficients are equal; such restrictions are not present when using the derived theorem. Using the derived theorem, as demonstration of its ease of use, stability criteria are derived without the imposed restriction on the relation between the control coefficients for three popular PSO variants.

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