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

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

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

Solving and GUI demonstration of traditional N-Queens Problem using Hill Climbing, Simulated Annealing, Local Beam Search, and Genetic Algorithm.

Solving Knapsack 0/1 problem with various Local Search algorithms like Hill Climbing, Genetic Algorithms, Simulated Annealing, Tabu Search

Examples are written in Java dedicated to JOpt-TourOptimizer

A Vehicle Routing Problem optimized solution with Variable Neighborhood Search (VNS), Variable Neighborhood Descent (VND) and Simulated Annealing

Hill Climbing / Simulated Annealing / Genetic / ...

Animation of VLSI Placement/Floorplanning using Simulated Annealing or Iterative Improvement

Evolutionary Computation Framework in Java

Homeworks from the Solving Optimization Problems Using Evolutionary Computation Algorithms in Java course.

Example programs for usage of the Chips-n-Salsa library

local search and optimization algorithms are used in AI with a map coloring example | AI course - Fall 2018

An Android App that solves the TSP using three different algorithms from the machine learning field.

SANOM: Simulated Annealing based Ontology Matcher

A multi-threading java implementation of meta-heuristic algorithms

Solving a TSP problem using Simulated Annealing algorithm from a 5x5 dataset

Examples for using DNA-Evolutions RESTful products

Experiments with simulated annealing and an optimized version of the modified lam adaptive annealing schedule