Draft:Goat Optimization Algorithm

Submission declined on 5 April 2025 by Pythoncoder (talk).

Your draft shows signs of having been generated by a large language model, such as ChatGPT. Their outputs usually have multiple issues that prevent them from meeting our guidelines on writing articles. These include:

Promotional tone, editorializing and other words to watch
Vague, generic, and speculative statements extrapolated from similar subjects
Essay-like writing
Hallucinations (plausible-sounding, but false information) and non-existent references
Close paraphrasing

Please address these issues. The best way to do it is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models.

If you would like to continue working on the submission, click on the "Edit" tab at the top of the window.
If you have not resolved the issues listed above, your draft will be declined again and potentially deleted.
If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors.
Please do not remove reviewer comments or this notice until the submission is accepted.

Where to get help

If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews.
If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed.

How to improve a draft

Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia.
Help:Wikitext – how to use the markup
Help:Referencing for beginners – how to include references
Wikipedia:Article development – how to develop your article
Wikipedia:Writing better articles – how to improve your article
Wikipedia:Verifiability – make sure your article includes reliable third-party sources

You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article.

Improving your odds of a speedy review

To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags.

Add tags to your draft

Editor resources

Easy tools: Citation bot (help) | Advanced: Fix bare URLs

Declined by Pythoncoder 3 days ago. Last edited by Pythoncoder 3 days ago. Reviewer: Inform author.

Resubmit

Please note that if the issues are not fixed, the draft will be declined again.

Goat Optimization Algorithm

The **Goat Optimization Algorithm (GOA)** is a bio-inspired metaheuristic algorithm proposed in 2025. It mimics the natural climbing behavior and problem-solving intelligence of mountain goats to solve complex global optimization problems. GOA is part of a growing family of nature-inspired optimization methods designed to find near-optimal solutions in high-dimensional and nonlinear search spaces.

1. Overview

GOA models the ability of goats to explore steep and rugged terrains while avoiding local traps and dynamically adapting to changes in the environment. The algorithm alternates between **exploration** and **exploitation** phases using mathematical modeling of goat behavior, such as random jumps, terrain sensing, and leader-following mechanisms.

The algorithm is particularly suitable for continuous optimization tasks and has been applied in engineering, machine learning, and operations research.

1. Key Features

- Bio-inspired from real-world goat behavior - Adaptive movement patterns - Balance between exploration and exploitation - Suitable for high-dimensional search spaces

1. Applications

GOA has been applied in:

- Engineering design optimization - Benchmark functions in global optimization - Machine learning parameter tuning - Resource allocation problems

1. Related Algorithms

- Particle Swarm Optimization (PSO) - Grey Wolf Optimizer (GWO) - Whale Optimization Algorithm (WOA) - Greedy Man Optimization Algorithm (GMOA)

1. References

1. Nozari, H., Abdi, H., Schmeltzer-Jarosch, A. (2025). *Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization*. **Applied Innovations in Industrial Management (AIIM)**. [1](https://www.iscihub.com/index.php/AIIM/article/view/37)

2. *Goat Optimization Algorithm*. The Science Archive. (2025). [2](https://thesciencearchive.org/2503-02331v1/)

3. Goat Optimization Algorithm – arXiv preprint. [3](https://doi.org/10.48550/arXiv.2503.02331)

4. GOA Code Repository. MathWorks. [4](https://www.mathworks.com/matlabcentral/fileexchange/180278-goat-optimization-algorithm-goa)

1. External links

- [ResearchGate publication](https://www.researchgate.net/publication/389495030_Goat_Optimization_Algorithm_A_Novel_Bio-Inspired_Metaheuristic_for_Global_Optimization) - [Academia.edu version](https://www.academia.edu/127960384/Goat_Optimization_Algorithm_A_Novel_Bio_Inspired_Metaheuristic_for_Global_Optimization)

1. See also

- Metaheuristic algorithms - Swarm intelligence - Evolutionary computation