semvec/pypi-packages huggingface dataset:
metaheuristicpy 3.1.0
A Python-based package of Metaheuristic Optimization Algorithms for Solving Continous and Discrete Optimization Problem
Requires Python: >=3.8
Keywords: Optimization, Metaheuristic, Swarm Intelligence, Evolutionary Algorithm, Soft Computing

# metaheuristicpy

A Python-based package of Metaheuristic Optimization Algorithms for Solving Continous and Discrete Optimization Problem

<hr>

# Cite

If you think this package is useful, please cite this paper in your project: <br>
[1] Bimantara, I.M.S., Sanjaya ER, N., Purwitasari, D. (2023). Character Entity Recognition Using Hybrid Binary-Particle Swarm Optimization and Conditional Random Field on Balinese Folklore Text. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_15 <br>
[2] Bimantara, I. M. S., & Widiartha, I. M. (2023). Optimization of K-Means Clustering Using Particle Swarm Optimization Algorithm for Grouping Traveler Reviews Data on Tripadvisor Sites. Jurnal Ilmiah Kursor, 12(1), 1-10. <br>
[3] Supriana, I. W., Raharja, M. A., Bimantara, I. M. S., & Bramantya, D. (2021). Implementasi dua model crossover pada algoritma genetika untuk optimasi penggunaan ruang perkuliahan. Jurnal RESISTOR (Rekayasa Sistem Komputer), 4(2), 167-177. <br>
[4] BIMANTARA, I Made Satria et al. Implementasi Generalized Learning Vector Quantization (GLVQ) dan Particle Swarm Optimization (PSO) Untuk Klasifikasi Kanker Payudara. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 10, n. 4, p. 307-318, may 2022. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/85746>. Date accessed: 31 may 2025. doi: https://doi.org/10.24843/JLK.2022.v10.i04.p01. <br>

# Contributors and Developer

- I Made Satria Bimantara
- email: satriabimantara.idm@gmail.com
KS1Beta 0.5.0
A C-extension library that outperforms NumPy via O(1) complexity.


# KS-Engine: O(1) Complexity via Mathematical Induction & Inline ASM

KS-Engine is a high-performance C-extension library designed to outperform traditional numerical libraries like NumPy for specific summation series.

### Technical Breakthroughs:
1. **Mathematical Optimization (Time Complexity: O(1))**:
   While NumPy calculates the sum of a sequence using O(N) iteration, KS-Engine utilizes mathematical induction (Arithmetic Progression algorithms) to resolve the result in constant time.

2. **Hardware-Level Acceleration (Inline Assembly)**:
   The core is written in pure x86-64 assembly integrated within C. By utilizing direct register manipulation (`rax`, `rdi`) and SSE/AVX instructions (`mulsd`, `cvtsi2sd`), it achieves the theoretical limit of the CPU's execution speed.

3. **Zero-Memory Mapping**:
   Unlike NumPy arrays that require substantial RAM allocation (O(N) space), KS-Engine operates with a constant memory footprint (0.00MB additional RSS), bypassing the Python heap and preventing memory fragmentation.

4. **Compiler-Specific Optimization**:
   Compiled exclusively with GCC using `-O3 -ffast-math -march=native`, ensuring that the binary is tailored specifically for the host CPU's microarchitecture.
nuitka_simple_gui 2026.1.31
A simple GUI application using Nuitka.

# [nuitka_simple_gui](https://github.com/ClericPy/nuitka_simple_gui)

[![PyPI](https://img.shields.io/pypi/v/nuitka_simple_gui?style=plastic)](https://pypi.org/project/nuitka_simple_gui/) ![PyPI - Wheel](https://img.shields.io/pypi/wheel/nuitka_simple_gui?style=plastic)


> `pip install nuitka_simple_gui`
>
> Then just run `python -m nuitka_simple_gui` or `nuitka_simple_gui`
>
> Or use uv `uvx nuitka_simple_gui` (if you have [uv](https://github.com/ultralytics/uv))
>

A simple GUI app for Nuitka.

## Features

1. Easily build your Python code into executable files using **Nuitka**
2. Handy shortcut buttons for common tasks
3. Compress folders into zip files
4. Quickly create a symbolic link for `start.exe`
5. Separate dependencies and source code—only build your source code
6. Added `onefile` mode (since 2023.07.18), with `keep cache` option for cached extraction(since 2025.9.23)
7. Added a beep notification after finishing on Windows (since 2023.07.18)
8. Added `dump-config` and `load-config` features (since 2023.07.27)
9. For more changes, check the changelog below
10. Fix Linux & macOS compatibility (since 2025.11.18)

## User Manual

No manual needed—just use the GUI!

## Documentation

No docs needed—just use the GUI!

## What's Next?

I'm still thinking about it. Stay tuned!

---

### WARNING

- Nuitka currently supports Python 3.6 to 3.12 (as of 2025-09-23). Python 3.13 is not supported yet.
- On Windows, Nuitka needs a C compiler. You can install [MinGW64](https://nuitka.net/doc/user-manual.html#mingw-windows) or [Visual Studio](https://nuitka.net/doc/user-manual.html#visual-c-compiler-windows).
  - If you don't have a C compiler, just run `python -m nuitka temp.py` and Nuitka will try to download MinGW64 for you automatically.

---

### Screenshot

![demo.png](https://raw.githubusercontent.com/ClericPy/nuitka_simple_gui/master/demo.png)

---

### Changelog

- 2025.9.23
  - Added a "keep cache" checkbox for `--onefile` mode
- 2025.1.7
  - Added a `nuitka_cache` button to show `NUITKA_CACHE_DIR`
  - Added support for `--jobs`
- 2025.1.6
  - Added tooltips to plugin checkboxes
  - Moved `ensure_python_path` log to the GUI textarea, for manual GCC downloads
- 2025.01.05
  - Fixed a failure when `pip_args` is empty
  - Made the Nuitka plugin list update dynamically