Metaheuristic-Flower_Pollination_Algorithm

Flower Pollination Algorithmn to Minimize Functions with Continuous Variables. The function returns: 1) An array containing the used value(s) for the target function and the output of the target function f(x). For example, if the function f(x1, x2) is used, then the array would be [x1, x2, f(x1, x2)].

• flowers = The population size. The Default Value is 3.

• p = Chance to create a global solution. The Default Value is 0.8.

• gamma = Global solution parameter. The Default Value is 0.5.

• lamb = Global solution parameter. The Default Value is 1.4.

• min_values = The minimum value that the variable(s) from a list can have. The default value is -5.

• max_values = The maximum value that the variable(s) from a list can have. The default value is 5.

• generations = The total number of iterations. The Default Value is 50.

• target_function = Function to be minimized.

Other Metaheuristics

Try online in the Colab my new library - pyMetaheuristic.

• Adaptive Random Search ( Colab Demo )
• Ant Lion Optimizer ( Colab Demo )
• Arithmetic Optimization Algorithm ( Colab Demo )
• Artificial Bee Colony Optimization ( Colab Demo )
• Artificial Fish Swarm Algorithm ( Colab Demo )
• Bat Algorithm ( Colab Demo )
• Biogeography Based Optimization ( Colab Demo )
• Cross-Entropy Method ( Colab Demo )
• Cuckoo Search ( Colab Demo )
• Differential Evolution ( Colab Demo )
• Dispersive Flies Optimization ( Colab Demo )
• Dragonfly Algorithm ( Colab Demo )
• Firefly Algorithm ( Colab Demo )
• Flow Direction Algorithm ( Colab Demo )
• Flower Pollination Algorithm ( Colab Demo )
• Genetic Algorithm ( Colab Demo )
• Grey Wolf Optimizer ( Colab Demo )
• Grasshopper Optimization Algorithm ( Colab Demo )
• Gravitational Search Algorithm ( Colab Demo )
• Harris Hawks Optimization ( Colab Demo )
• Improved Grey Wolf Optimizer ( Colab Demo )
• Improved Whale Optimization Algorithm ( Colab Demo )
• Jaya ( Colab Demo )
• Jellyfish Search Optimizer ( Colab Demo )
• Memetic Algorithm ( Colab Demo )
• Moth Flame Optimization ( Colab Demo )
• Multiverse Optimizer ( Colab Demo )
• Particle Swarm Optimization ( Colab Demo )
• Random Search ( Colab Demo )
• Salp Swarm Algorithm ( Colab Demo )
• Simulated Annealing ( Colab Demo )
• Sine Cosine Algorithm ( Colab Demo )
• Whale Optimization Algorithm ( Colab Demo )

Multiobjective Optimization or Many Objectives Optimization

For Multiobjective Optimization or Many Objectives Optimization try pyMultiobjective

TSP (Travelling Salesman Problem)

For Travelling Salesman Problems try pyCombinatorial