The repository I created to help me study for a course of my master :)
Python 100.00%
metaheuristics's Introduction
MetaHeuristics
Metaheuristics implemented
Genetic Algorithm (GA)
Local Search (SL)
Iterated Local Search (ILS)
Population Base Incremental Learning (PBIL)
Simulated Annealing (SA)
Tabu Search (TS)
Artificial Bee Colony (ABC)
Knapsack Problem
pythonmain_KSP.py
Knapsack Problem performance
pythonmain_statis_KSP.py
1D function Problem
pythonmain.py
1D function Problem performance
pythonmain_statis.py
Biography
Simulated annealing: From basics to applications (Daniel Delahaye, Supatcha Chaimatanan, Marcel Mongeau)
Iterated Local Search: Framework and Applications (Helena Ramalhinho Lourenco, Thomas Stuzle, Olivier C Martin)
An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics (Shumeet Baluja)
An Efficient Algorithm for the Knapsack Sharing Problem (Mhand Hifi, Slim Sadfi, Abdelkader Shibi)
An Overview of Genetic Algorithms: Part 1, Fundamentals (David Beasley, David R.Bull, Ralph R. Martin)
Comparison of Metaheuristics (John Silberholz and Bruce Golden)
Removing the Genetics from the Standard Genetic Algorithm
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems (Dervis Karaboga and Bahriye Basturk)
Biography not yet implemented
Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem
On the Neighborhood Structure of the Traveling Salesman Problem Generated by Local Search Moves (Günther Stattenberger, Markus Dankesreiter, Florian Baumgartner, Johannes J.Schneider)
MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with time windows