Code for paper "Self-Directed Online Machine Learning for Topology Optimization"

Simulated Annealing based fixed-die standard cell placement engine for IBM benchmark netlists

Solving Vehicle Routing Problem with Time Windows using Hillclimbing, Genetic and Simulated Annealing algorithms + Push Foward Insert Heuristic

N-Queen(s) Problem implemented using Simulated Annealing Algorithm in Python Language

FIB-IA 2022-23 Q2 Artificial Intelligence subject

A Python package to solve Countdown's number round using metaheuristics.

Project for CS 170 - Algorithms at UC Berkeley

Courseworks of VLSI Physical Design Automation, NTHU CS course teached by prof. Ting-Chi Wang in fall 2023

Projeto e implementação de um sistema para resolução do problema de alocação de horários utilizando a meta-heurística Simulated Annealing. Trabalho realizado no 5º período de Ciência da Computação do Instituto Federal de Minas Gerais(IFMG) - Campus Formiga para a Disciplina Inteligência Artificial.

This is a multi-destination route optimization app, developed as a Final Year Project for a Computer Engineering degree, which received a grade of 10 with honors.

Put people in the right places at working using 3 optimization algorithms (Ant Colony Optimization, Simmulated Annealing and Genetic Algorithm built from the ground up with Racket)

Solving TSP with metaheuristic and exact algorithms.

Implementation of Hill Climber and Simulated Annealing algorithms

Extreme Learning Machine and Meta-heuristic algorithms implemented in C using threads to solve a Feature Selection problem related to the generation of Seawave Energy

A C++ program to solve the Traveling Salesman Problem using Simulated Annealing. The program reads city coordinates from a .tsp file, performs optimization, and outputs an approximate shortest tour.

The objective of this practice is to solve a problem of a Distributed File System using two local search algorithms. Hill Climbing Simulated Annealing.

This app calculate the optimized route between multiple locations added by user on google maps. stating position and ending position remains same. It gives you a optimized sequence of places to travel so that all places could be covered in minimum distance exactly once. This project is sub part of my another project Carbon Footprint Calculator.

Optimization software for QI Lab. Interfaces with hardware and uses Simulated Annealing Optimization to maximize photon count.