Stochastic stability analysis of composite dynamic system for particle swarm optimization
• Four types of forces acting on particles are extracted from the inherent kinematic analogy of PSO’s nature. • The stochastic composite dynamic model for PSO is proposed. • The stochastic stability of PSO is analyzed via constructing the corresponding Lyapunov functions. • The different influences...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Wen Yong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Umfang: |
17 |
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| Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
|---|---|
| Übergeordnetes Werk: |
volume:592 ; year:2022 ; pages:227-243 ; extent:17 |
| Links: |
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| DOI / URN: |
10.1016/j.ins.2021.12.095 |
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ELV056977700 |
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| 520 | |a • Four types of forces acting on particles are extracted from the inherent kinematic analogy of PSO’s nature. • The stochastic composite dynamic model for PSO is proposed. • The stochastic stability of PSO is analyzed via constructing the corresponding Lyapunov functions. • The different influences from optimization functions to PSO’s stability are discussed. • The convergence time is defined to measure the computing efficiency of PSO. | ||
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• Four types of forces acting on particles are extracted from the inherent kinematic analogy of PSO’s nature. • The stochastic composite dynamic model for PSO is proposed. • The stochastic stability of PSO is analyzed via constructing the corresponding Lyapunov functions. • The different influences from optimization functions to PSO’s stability are discussed. • The convergence time is defined to measure the computing efficiency of PSO. |
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• Four types of forces acting on particles are extracted from the inherent kinematic analogy of PSO’s nature. • The stochastic composite dynamic model for PSO is proposed. • The stochastic stability of PSO is analyzed via constructing the corresponding Lyapunov functions. • The different influences from optimization functions to PSO’s stability are discussed. • The convergence time is defined to measure the computing efficiency of PSO. |
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• Four types of forces acting on particles are extracted from the inherent kinematic analogy of PSO’s nature. • The stochastic composite dynamic model for PSO is proposed. • The stochastic stability of PSO is analyzed via constructing the corresponding Lyapunov functions. • The different influences from optimization functions to PSO’s stability are discussed. • The convergence time is defined to measure the computing efficiency of PSO. |
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Stochastic stability analysis of composite dynamic system for particle swarm optimization |
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Zhang, Ran Ran |
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| doi_str |
10.1016/j.ins.2021.12.095 |
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2024-07-06T21:56:18.286Z |
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