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CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search — arXiv:2609.35657

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35657
License (as archived): CC0-1.0
Archived: 2026-10-07T21:03:51.962Z
SHA-256 of the archived text: 7dfd5342daf093fc598291c82d761b95a0df679d70bb0f757b3e2be3d3ebe540
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How to cite

CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search — arXiv:2609.35657 — arXiv (en), https://arxiv.org/abs/2609.35657 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_37d82e481ebd (Metraipolis archive dc_37d82e481ebd, sha256 7dfd5342daf093fc598291c82d761b95a0df679d70bb0f757b3e2be3d3ebe540)

BibTeX

@misc{dc37d82e481ebd,
  title = {CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search — arXiv:2609.35657},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35657},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_37d82e481ebd; sha256 7dfd5342daf093fc598291c82d761b95a0df679d70bb0f757b3e2be3d3ebe540},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35657. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Population-based optimization methods often use previous search information through successful solutions, parameter adaptation, or operator performance, but they rarely retain the context in which a search behavior succeeded or failed. We introduce Cognitive Memory-Driven Optimization (CMDO), a derivative-free population-based optimizer that represents experience as the relationship between search context, search behavior, and observed outcome. CMDO organizes these experiences across working, episodic, and consolidated memory, retrieves them according to similarity with the current search state, and uses both positive and negative evidence to guide subsequent search. Retrieved experience does not replay previous candidate locations; instead, it selects search recipes that are reconstructed from the current population through exploratory, directed, and local search behaviors with adaptive search geometry. We evaluate CMDO on selected Blackbox Optimization Benchmarking test suite on COCO (BBOB/COCO) and Congress on Evolutionary Computation 2017 (CEC2017) problems against DE, CMA-ES, SHADE, GWO, HHO, and ORCA, and further study its application to seven-parameter photovoltaic model estimation using measured current--voltage data. The results show problem-dependent but competitive optimization performance, including the lowest median error among the compared methods on CEC2017 F10. More importantly, analysis of the search traces shows that context-dependent recall changes the distribution of executed search behaviors, while unsuccessful experiences remain available as negative evidence for later decisions, showing that accumulated experience directly influences subsequent search behavior. These results support the use of explicit context--behavior--outcome memory as an active mechanism for controlling population-based search.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.35657 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:03.420Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).