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

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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 (8 months ago)
• Default Branch: main
• Last Pushed: 2024-09-07T08:09:46.000Z (2 months ago)
• Last Synced: 2024-09-26T07:20:45.970Z (about 2 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:

bash

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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.