Implemented fully documented Particle Swarm Optimization algorithm (basic model with few advanced features) using Python programming language

To implement Optimization (maximization) problem through Linear programming in Python Language.

The library provides a general genetic algorithm. It is simple, easy to use, and very fast. All you need to do is to define the fitness function and its variables. There are many examples of how to deal with classic genetic algorithms problems.

Multi-period Home Healthcare Routing Problem (HHCRP) with qualification, synchronization and time windows constraints.

A multi-threaded Simulated Annealing core in C

Maximize revenues of Online Retail Business with Thompson Sampling algorithm

solution of problems !!

An Evolutionary Algorithm written in JavaScript that can take in any generic module representing a problem to be optimized

Great repository to learn about particle swarm optimization. It provides the basic tools to automatically for optimization / fitting, etc.

Simulation of the Secretary Problem

A modified Traveling Salesman Problem (TSP) optimization where a directed graph tour starting and ending at the first node is chosen so to maximize a custom objective function (net profit)

Profit Maximisation Function using GA

Linear Programming Problem Solver

Lightweight Tool for Genetic Algorithms in Python

A comprehensive collection of implementations and resources related to the SIMPLEX algorithm.

Calculating the maximum semi-ellipsoid volume using Reduced Gradient Descent method with total surface area given as a constraint.

Python program to solve problems using the simplex method, with options for graphical mode and dual method, addressing both maximization and minimization problems.

SimplexCPP is a c++ library that helps solves `Linear Programming Equations` i.e Maximization problems, easily also providing you with neat results.

A series of genetic algorithms in Common Lisp and Racket, evolving from simple to more complex ones