A genetic algorithm-based tool designed to enhance circuit configurations for optimal recovery and purity of valuable materials in mineral processing.

AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

This project applies Simulated Annealing to solve the Traveling Salesman Problem using Peru's departments as nodes. Through iterative refinement, it finds the shortest route visiting each department once. Visual feedback enhances understanding and debugging, resulting in an optimal solution displayed with total distance.

Morph an input dataset of 2D points into select shapes, while preserving the summary statistics to a given number of decimal points through simulated annealing.

Algorithms for creating short forms based on psychometric principles.

Symmetry crystal combinatorial optimization program for crystal prediction.

High-performance metaheuristics for optimization coded purely in Julia.

OpenJij : Framework for the Ising model and QUBO.

UAB The Hack - Caixa Enginyers Challenge, 2n prize winners. Challenge: generate a route for some vans to visit some cities in Catalunya with restrictions.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

UAB The Hack - Caixa Enginyers Challenge, 2n prize winners. Challenge: generate a route for some vans to visit some cities in Catalunya with restrictions.

A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

Simulated annealing ruby implementation

Sudoku Solver Using Parallel Simulated Annealing

Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.

This Python package provides implementations of three metaheuristic algorithms to solve the Traveling Salesman Problem (TSP): Steepest Ascent Hill Climbing, Simulated Annealing, and Ant Colony Optimization.

This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Logic Locking Attacks via Synthesis Tuning

⚡Solve the Eight Queens Puzzle with Simulated Annealing for optimal queen placement on a chessboard

Killer Sudoku Program Using Simulated Annealing, Backtracking, and Backjumping Algorithm