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The Educational Competition Optimizer

ECO

24 papers tagged archive 2025-07-28

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

In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost its efficiency, the algorithm divides the iterative process into three distinct phases: elementary, middle, and high school. Through this stepwise approach, ECO gradually narrows down the pool of potential solutions, mirroring the gradual competition witnessed within educational systems. This strategic approach ensures a smooth and resourceful transition between ECO's exploration and exploitation phases. The results indicate that ECO attains its peak optimization performance when configured with a population size of 40. Notably, the algorithm's optimization efficacy does not exhibit a strictly linear correlation with population size. To comprehensively evaluate ECO's effectiveness and convergence characteristics, we conducted a rigorous comparative analysis, comparing ECO against nine state-of-the-art metaheuristic algorithms. ECO's remarkable success in efficiently addressing complex optimization problems underscores its potential applicability across diverse real-world domains. The additional resources and open-source code for the proposed ECO can be accessed at https://aliasgharheidari.com/ECO.html and https://github.com/junbolian/ECO.

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

24 shown of 24, 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 22 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
Visual Tracking4
Object Tracking3
Visual Object Tracking2
Action Recognition1
Articles1
Domain Adaptation1
Feature Compression1
Image Captioning1
Management1
Navigate1
Object1
Re-Ranking1
Reinforcement Learning1
Reinforcement Learning (RL)1
Scheduling1
Segmentation1
Super-Resolution1
Temporal Action Localization1
Word Embeddings1
Zero-Shot Learning1

Usage over time archive 2025-07-28

Papers per year tagged with ECO: 2012 to 2025, peak 5 5 0 2012: 1 paper 2012 2013: 0 papers 2013 2014: 0 papers 2014 2015: 0 papers 2015 2016: 1 paper 2016 2017: 1 paper 2017 2018: 1 paper 2018 2019: 5 papers 2019 2020: 1 paper 2020 2021: 5 papers 2021 2022: 1 paper 2022 2023: 1 paper 2023 2024: 4 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (24 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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