Optimization Algorithm Toolkit - OAT 1.3

Ease working with classical and state-of-the-art optimization algorithms on standard benchmark problem domains The Optimization Algorithm Toolkit (OAT) is a workbench and toolkit for developing, evaluating, experimenting, and playing with classical and state-of-the-art optimization algorithms on standard benchmark problem domains; including reference algorithm implementations (Java), graphing, visualizations and much more.
Main features:
- Graphical User Interface (GUI) with graphs, configuration dialogs, visualizations and much more for the research scientist and interested novice alike.
- Fully featured Application Programming Interface (API) for experimentation, implementation of additional algorithms and problem domains.
- Source code is included to help you learn more about how optimization algorithms work, and to contribute new algorithms and problems to the project.
- Example code to get you up and running quickly and unit tests to ensure algorithm robustness and consistency.
- Provides a focus on computational intelligence and biologically inspired optimization algorithms such as evolutionary algorithms, artificial immune system algorithms, particle swarm optimization, ant colony optimization, and much more.
- Provides literature references for all algorithm implementations (See a full algorithm listing with references).
- Problem Domains Include:
- Continuous Function Optimization, including 24 Algorithms not limited to Evolutionary Algorithms, Artificial Immune System Algorithms, Physics Algorithms, Swarm Algorithms, and Random Algorithms. 69 problem instances not limited to classic functions such as De Jongs F-series, Ackley, Easom, Goldstein, Griewangk, Langermann, Michalewicz, Rastrigin, Rosenbrocks Valley, Schwefel, Six Hump Camel Back, Three Pot Holes, Mahfouds M-series, Bohachevsky, Hansen, Shubert, and many others.
- Huygens Search & Optimization Benchmark Suite: all function optimization algorithms, billions of fractal optimization instances, benchmark server, and much more
- Traveling Salesman Problem (TSP), including 10 algorithms not limited to Ant Colony Optimization, Evolutionary Algorithms, Artificial Immune System Algorithms, and Random Algorithms. 15 problem instances from TSPLib including Berlin52, Burma14, Ulysses22, and many others.
- Protein Folding (Protein Structure Prediction - PSP), including 3 algorithms including Evolutionary Algorithms, and Random Algorithms. 14 problem instances all examples of Dills Hydrophobic-Polar (HP) Model
- Binary Optimization Functions including 6 algorithms not limited to Evolutionary Algorithms, Bit Hill-Climbers, Artificial Immune System Algorithms, and Random Algorithms. 10 problem instances not limited to Trap Functions, Deceptive Functions, Massively Multimodal, OneMax, and others.
- Graph Coloring Problem (GCP), including 2 algorithms including an Artificial Immune System algorithm, 56 problem instances all benchmark instances from the GCP repository on Michael Tricks Operations Research page
- Binary Character Recognition (BCR): 2 algorithms, 1 problem instance (lippman binary character recognition)
System requirements:
- Java