{"id":15138140,"url":"https://github.com/u-c4n/jecso-mopso","last_synced_at":"2026-02-22T13:46:01.104Z","repository":{"id":229145605,"uuid":"775880476","full_name":"U-C4N/JECSO-MOPSO","owner":"U-C4N","description":"JECSO-MOPSO is a specialized multi-objective optimization algorithm for jet engine component sizing. 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This repository contains the implementation of JECSO-MOPSO in Python, along with sample test problems and a real-world jet engine design case study.\n\n## Features\n\nJECSO-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:\n\n1. **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.\n\n2. **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.\n\n3. **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.\n\n## Installation\n\nTo use JECSO-MOPSO, follow these steps:\n\n1. Clone the repository:\n  ```bash\n  git clone https://github.com/C1N-S4/JECSO-MOPSO.git\nInstall the required dependencies:\nbash\n\n\nCopy code\npip install -r requirements.txt\nUsage\nThe repository provides a user-friendly interface to run the JECSO-MOPSO algorithm on various optimization problems. Here's a basic example:\n\npython\n\n\nCopy code\nfrom jecso_mopso import JECSO_MOPSO\n\n# Define the optimization problem\nproblem = YourOptimizationProblem()\n\n# Set the algorithm parameters\nmax_iterations = 100\npopulation_size = 50\n# ...\n\n# Create an instance of JECSO-MOPSO\noptimizer = JECSO_MOPSO(problem, max_iterations, population_size, ...)\n\n# Run the optimization\npareto_front = optimizer.optimize()\n\n# Visualize the results\noptimizer.plot_pareto_front()\nFor more detailed usage instructions and examples, please refer to the documentation.\n\nTest Problems\nThe repository includes several benchmark test problems to evaluate the performance of JECSO-MOPSO:\n\nZDT1: A bi-objective test problem with 30 decision variables.\nDTLZ2: A scalable test problem with a configurable number of objectives.\nJet Engine Design Problem: A real-world case study of optimizing the component sizes of a turbofan engine.\nThese test problems can be found in the test_problems directory.\n\n\u003ca href=\"https://discord.gg/2Tutcj6u\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://raw.githubusercontent.com/tandpfun/skill-icons/main/icons/Discord.svg\" height=\"30\" width=\"40\" /\u003e\u003c/a\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fu-c4n%2Fjecso-mopso","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fu-c4n%2Fjecso-mopso","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fu-c4n%2Fjecso-mopso/lists"}