MetaZoo

A zoo of metaheuristic algorithms (please do not feed them) for optimization.

Warning

This library is in preview and is not ready for production use.

We're working hard to make this library stable and feature-complete, but until then, expect to encounter bugs, missing features, and fatal errors.

Modules

• Bio-inspired

  • [-] Evolutionary Computation
    • Genetic Algorithm (GA)
    • Evolution Strategies (ES)
    • Evolutionary Programming (EP)
    • Genetic Programming (GP)
    • Differential Evolution (DE)
  • Darwin (Swarm Intelligence and Other Animal-inspired Algorithms)
    • Swarm
      • Particle Swarm Optimization (PSO)
      • Ant Colony Optimization (ACO)
      • Artificial Bee Colony (ABC)
      • Firefly Algorithm (FA)
    • Animals
      • Cheetah Algorithm (CA)
      • Elephant Herding Optimization (EHO)
      • Lion Optimization Algorithm (LOA)
      • Wolf Search Algorithm (WSA)
      • Wild Horse Optimizer (WHO)

• Physics-inspired

  • Phenomena Inspired
    • Simulated Annealing (SA)
  • Music-inspired
    • Harmony Search (HS)

• Human-inspired

  • Social System-inspired
    • Tabu Search (TS)
    • Culture Algorithm (CA)

Learning Resources

Petting zoo

PettingZoo is a small Streamlit app for visualizing and interacting with MetaZoo library.

It provides a user-friendly interface for exploring the various metaheuristic algorithms implemented in the MetaZoo library, allowing users to easily experiment with different algorithms and parameters.

Playgrounds

Playgrounds is a collection of interactive Jupyter notebooks for experimenting with the MetaZoo library.

It provides a hands-on environment for users to explore and visualize the behavior of different metaheuristic algorithms.

Architecture

graph TD

    6["User<br>External Actor"]
    subgraph 1["Core Library<br>Python"]
        14["Gym Environment<br>Python"]
        subgraph 2["Bio Module<br>Python"]
            subgraph 3["Evolutionary Algorithms<br>Python"]
                10["Evolutionary Utilities<br>Python"]
                9["Genetic Algorithm<br>Python"]
                subgraph 4["Operators<br>Python"]
                    11["Crossover Operators<br>Python"]
                    12["Mutation Operators<br>Python"]
                    13["Selection Operators<br>Python"]
                end
                %% Edges at this level (grouped by source)
                9["Genetic Algorithm<br>Python"] -->|Uses| 4["Genetic Operators<br>Python"]
                9["Genetic Algorithm<br>Python"] -->|Uses| 10["Evolutionary Utilities<br>Python"]
            end
            %% Edges at this level (grouped by source)
            9["Genetic Algorithm<br>Python"] -->|Evaluates in| 14["Gym Environment<br>Python"]
        end
    end
    subgraph 5["Application/Service<br>Python"]
        7["Main Application<br>Python"]
    end
    %% Edges at this level (grouped by source)
    5["Application/Service<br>Python"] -->|Leverages| 1["Core Library<br>Python"]
    6["User<br>External Actor"] -->|Interacts with| 5["Application/Service<br>Python"]

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