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INFO: An Efficient Optimization Algorithm based on Weighted Mean of Vectors

INFO

36 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors’ position using three core procedures: updating rule, vector combining, and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions, and five constrained engineering test cases including optimal design of 10-reservoir system and 4-reservoir system. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of INFO algorithm are publicly available at https://aliasgharheidari.com/INFO.html

Code snippet in the archive: a link on aliasgharheidari.com (archive link, not checked and not linked: not a code host this site links to).

Papers archive 2025-07-28

30 shown of 36, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 86 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Contrastive Learning3
Retrieval3
Action Recognition2
Deep Learning2
Language Modeling2
Language Modelling2
Logical Reasoning2
Object Detection2
Question Answering2
Representation Learning2
Semantic Segmentation2
Sentence2
feature selection2
object-detection2
3D Reconstruction1
3D Scene Reconstruction1
3D geometry1
Answer Selection1
Articles1
Autonomous Driving1

Usage over time archive 2025-07-28

Papers per year tagged with INFO: 2013 to 2025, peak 7 7 0 2013: 2 papers 2013 2014: 0 papers 2014 2015: 1 paper 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 2 papers 2018 2019: 3 papers 2019 2020: 6 papers 2020 2021: 4 papers 2021 2022: 7 papers 2022 2023: 3 papers 2023 2024: 6 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (36 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Stochastic Optimization

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