Localized KBO with genetic dynamics for multi-modal optimization
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915012027613184 |
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| author | Ferrarese, Federica Totzeck, Claudia |
| author_facet | Ferrarese, Federica Totzeck, Claudia |
| contents | In this paper, we introduce a novel approach to multi-modal optimization by enhancing the recently developed kinetic-based optimization (KBO) method with genetic dynamics (GKBO). The proposed method targets objective functions with multiple global minima, addressing a critical need in fields like engineering design, machine learning, and bioinformatics. By incorpo rating leader-follower dynamics and localized interactions, the algorithm efficiently navigates high-dimensional search spaces to detect multiple optimal solutions. After providing a binary description, a mean-field approximation is derived, and different numerical experiments are conducted to validate the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Localized KBO with genetic dynamics for multi-modal optimization Ferrarese, Federica Totzeck, Claudia Numerical Analysis In this paper, we introduce a novel approach to multi-modal optimization by enhancing the recently developed kinetic-based optimization (KBO) method with genetic dynamics (GKBO). The proposed method targets objective functions with multiple global minima, addressing a critical need in fields like engineering design, machine learning, and bioinformatics. By incorpo rating leader-follower dynamics and localized interactions, the algorithm efficiently navigates high-dimensional search spaces to detect multiple optimal solutions. After providing a binary description, a mean-field approximation is derived, and different numerical experiments are conducted to validate the results. |
| title | Localized KBO with genetic dynamics for multi-modal optimization |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2411.04840 |