A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

A powerful bin packing problem solver for any polygons

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

Efficient Ceph placement optimization, aiming for maximum storage capacity through equal OSD utilization.

This repository contains code with tutorials of how to approach optimization problems using quantum computing.

Optimize revenues through pricing algorithm in python - Demand with uniform distribution

EARS is a free and open-source Java-based framework for ranking, developing, and experimenting with single- and multi-objective evolutionary algorithms

Proposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers.

Optimization of aircraft short-term trajectories, parameterized using B-splines.

ArchGeo library code for the paper "Discrete Orthogonal Structures"

Generators for Combinatorial Optimization

Feasibility study on using mixed-integer-linear programming for solving optimal pump scheduling problem in water distribution networks.

Transportation Model for an optimal strategy for distributing a commodity from a group of supply centers to various receiving centers. Solved in C# Windows Form Application

The model of Policy Priority Inference

SDPLIB is a collection of semidefinite programming test problems.

This repository holds the content for the GECCO 2025 competition on evolutionary algorithms for Earth Observations Satellite Scheduling!

Mock IPL auction - Automated decision making

Optimisation problem library

Project to Solve a SVM fitting problem in MATLAB using a consensus distributed ADMM approach and CVX. Two SMW has been trained and tested on different datasets.

A repository dedicated to the mathematical modeling and solution of optimization problems, featuring practical examples in Stochastic Programming, Linear Programming (LP), and Mixed-Integer Linear Programming (MILP)