Programming Optimization and Metaheuristics

This repository contains a collection of optimization problems and their solutions using Pyomo and various metaheuristic algorithms. The repository covers different types of optimization problems such as:

• Linear Programming (LP)
• Nonlinear Programming (NLP)
• Mixed Integer Linear Programming (MILP)
• Mixed Integer Nonlinear Programming (MINLP)
• Metaheuristic Algorithms (Simulated Annealing, Genetic Algorithm, etc.)

Overview

Optimization is a crucial part of solving real-world problems across various domains, including supply chain management, financial planning, machine learning, and engineering. This repository demonstrates how to solve optimization problems using the powerful Pyomo modeling framework and explore the use of metaheuristics for complex, large-scale problems.

Key Features:

• Pyomo: A Python-based optimization modeling language for formulating and solving linear, nonlinear, and mixed-integer optimization problems.
• Metaheuristics: A collection of algorithms inspired by nature and evolution, such as Genetic Algorithms (GA), Simulated Annealing (SA), and more.
• Solvers: Integration with several solvers like CBC, Gurobi, CPLEX, IPOPT, and others for solving the formulated models.