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https://github.com/u-c4n/jecso-mopso
JECSO-MOPSO is a specialized multi-objective optimization algorithm for jet engine component sizing. It enhances the standard MOPSO algorithm to efficiently handle jet engine design constraints and improve Pareto-optimal solutions.
https://github.com/u-c4n/jecso-mopso
defance mechanical-engineering rocket space
Last synced: 2 months ago
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JECSO-MOPSO is a specialized multi-objective optimization algorithm for jet engine component sizing. It enhances the standard MOPSO algorithm to efficiently handle jet engine design constraints and improve Pareto-optimal solutions.
- Host: GitHub
- URL: https://github.com/u-c4n/jecso-mopso
- Owner: U-C4N
- Created: 2024-03-22T08:24:51.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T08:09:46.000Z (4 months ago)
- Last Synced: 2024-09-26T07:20:45.970Z (3 months ago)
- Topics: defance, mechanical-engineering, rocket, space
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# JECSO-MOPSO
JECSO-MOPSO (Jet Engine Component Sizing Optimization using Multi-Objective Particle Swarm Optimization) is a novel optimization algorithm specifically designed for the multi-objective optimization of jet engine components. This repository contains the implementation of JECSO-MOPSO in Python, along with sample test problems and a real-world jet engine design case study.
## Features
JECSO-MOPSO adapts and enhances the standard Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to address the specific requirements of jet engine design optimization. The algorithm incorporates the following key features:
1. **Adapted velocity and position update equations:** JECSO-MOPSO modifies the standard MOPSO equations to consider the physical properties of jet engine components, enabling more effective exploration of the design space.
2. **Local search strategy:** A local search procedure is employed to improve solutions on the Pareto front, enhancing the convergence and diversity of the optimization process.
3. **Specialized constraint handling mechanism:** JECSO-MOPSO includes a tailored constraint handling technique that efficiently deals with the complex constraints encountered in jet engine design, ensuring feasible and high-quality solutions.
## Installation
To use JECSO-MOPSO, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/C1N-S4/JECSO-MOPSO.git
Install the required dependencies:
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pip install -r requirements.txt
Usage
The repository provides a user-friendly interface to run the JECSO-MOPSO algorithm on various optimization problems. Here's a basic example:python
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from jecso_mopso import JECSO_MOPSO# Define the optimization problem
problem = YourOptimizationProblem()# Set the algorithm parameters
max_iterations = 100
population_size = 50
# ...# Create an instance of JECSO-MOPSO
optimizer = JECSO_MOPSO(problem, max_iterations, population_size, ...)# Run the optimization
pareto_front = optimizer.optimize()# Visualize the results
optimizer.plot_pareto_front()
For more detailed usage instructions and examples, please refer to the documentation.Test Problems
The repository includes several benchmark test problems to evaluate the performance of JECSO-MOPSO:ZDT1: A bi-objective test problem with 30 decision variables.
DTLZ2: A scalable test problem with a configurable number of objectives.
Jet Engine Design Problem: A real-world case study of optimizing the component sizes of a turbofan engine.
These test problems can be found in the test_problems directory.