Feloopy

What is new?

- Project has moved from https://github.com/ktafakkori/feloopy to https://github.com/feloopy/feloopy
- Introducing `track_history=False/True` as a new search attribute, allowing visualization of trends using heuristic optimization algorithms.
- Introducing `flp.parallel_search()` to parallelize the solution environments over threads and processes.
- Introducing environment-based usage of multi-attribute decision-making methods.
- `m.sets()` or `flp.sets()` now accepts one or more than one set in defining for loops.
- `m.sum()` is now the best way to have summation expressions in the model.
- Fixed bugs in displaying decision variables.
- Fixed bugs in defining and using zero-dimensional variables in heuristic optimization.
- Updated the solvers (i.e., algorithms) provided by the `niapy` and `mealpy` heuristic optimization interfaces.
- Updated free solvers installable by `flp setup` or through the [FelooPy Engine](https://github.com/feloopy/engine).
- Introducing `save_io` as a new method for the data toolkit.
- Testing grad-heuristics (gradient-based optimizers of the machine learning field) for upcoming versions.
- Enhancing the consistency between FelooPy and the FelooPy engine.
- Numerous bug fixes and feature improvements.

**What is new?**

- Complex variable definitions.
- String constraint definitions.
- Improved Pareto-front generation for multi-objective exact optimization.
- Support for many-objective exact optimization.
- New multi-attribute decision-making methods in the FelooPy 0.3.0+ environment.
- Enhanced support for Python and Julia in optimization workflows.
- New exact optimization interfaces: `jump`, `mathopt`, and `pyoptinterface`.
- Model generation time reports.
- Enhanced data toolkit.
- Improved I/O data management.
- Better support for automatic linearization of constraint and objective terms.
- Resolved benchmarking and sensitivity analysis issues.
- Fixed issues with multi-objective heuristic optimization.
- Fixed issues with similarity analysis.
- Fixed errors in special constraints and linearization processes.
- Bug fixes and efficiency improvements.
- Planning to transition from environment-free to environment-based modeling.
- And more!

**Exciting News!**

FelooPy has introduced a new feature: the **Engine**. This tool simplifies the process of:

- Installing the CPython interpreter
- Creating a virtual environment
- Installing and updating the FelooPy package

**Important Notes:**
- This software (i.e., FelooPy Engine) is currently a work in progress and requires a reliable internet connection to interact with the official [Python.org](https://www.python.org/) and [PyPI.org](https://pypi.org/) websites.
- Supported Operating System: **Windows**
- After setting up the engine, if the PATH environment variable is not set, you need to configure it manually. Add "C:/Users/Username/Environments/flp-310/Scripts" (or the default path if it hasn't been changed) to your PATH variable. You might also try running the app with administrator permissions. For more information, refer to the feloopy_config.txt file, which is generated alongside feloopy-engine.exe after it runs.

You can download the file `feloopy-engine.exe` below. This software is portable and does not require installation.

| OS | Download |
|---------------|-------------------------------------------------------------------------------------------------------------------------------------|
| Windows | [](https://github.com/ktafakkori/feloopy/releases/download/0.3.5/feloopy-engine.exe)|

**What is new?**

* Introducing the `search` class, your all-in-one problem solver!
* Introducing decision environments, allowing for complex modeling and computation tasks and workflows.
* Introducing the improved data toolkit. A class to handle the inputs and outputs of the decision environments.
* Supporting new exact optimization interfaces: `highs`, `insideopt`, and `gams`.
* Supporting multi-objective exact optimization: Normalized weighted sum method (nwsm) and epsilon-constraint method (ecm).
* Supporting multi-parameter sensitivity and similarity analysis.
* Supporting benchmarking interfaces and solvers.
* Introducing a new report interface with clear and structured sections.
* Supporting structured constraint definition.
* Removing bugs and enhancing efficiency.
* Documenting the methods and classes.
* And more!

* This release was experimental for testing the next release.

* **Introducing command line interface tools for easier interaction.**
* **Embedded heuristic optimization algorithms have been revised for better performance.**
* **Full support for multi-attribute decision making has been achieved, thanks to the integration with `pydecision`.**
* **The IIS functionality of supported interfaces has been fixed.**
* **Support for new optimization packages including `niapy` and `pygad` has been added.**
* **Various bugs have been fixed and the user experience has been improved.**
* **Stability and reliability of the software have been enhanced.**
* **Compatibility with newer versions of optimization interfaces has been ensured.**
* **A mathematical model report has been added for better debugging.**
* **A data toolkit has been introduced for data manipulation needs.**
* **The link to interface (lti) method has been introduced for seamless integration.**
* **Performance of the software has been optimized for better speed and efficiency.**
* **This is the most stable release since the introduction of FelooPy.**

Major Enhancements

- **Uncertain Optimization Support**: Can now solve uncertain optimization problems.
- **Improved Reporting**: Generates more detailed reports with additional specifications. These reports can be saved as .txt files for future reference.
- **Better Linearizers Support**: Improved linearizers support, enhancing the overall performance.
- **Single and Multi-objective Optimization**: Full support for both single- and multi-objective optimization problems.
- **New Heuristic Optimization Algorithms**: Supports new heuristic optimization algorithms, providing more options for problem-solving.

User Interface Enhancements

- **Improved Decision/Objective Values Display**: Fixed errors in displaying decisions or objective values, providing a more accurate and intuitive user experience.
- **Improved Report UI**: The user interface for reports has been redesigned and improved, making it easier to understand the output of your optimization problems.

Stability Improvements

- **Fewer Bugs and Better Stability**: Fixed some bugs and improved the overall stability of the software.
- **Enhanced Constraint and Convex Optimization**: Improved the support for constraint and convex optimization, making it more robust and reliable.

Beta Features

- **LocalSolver Support (Beta)**: Testing support for LocalSolver; stay tuned for updates.
- **Pymoo Support (Beta)**: Testing support for Pymoo. Look forward to this feature in upcoming releases.