Snippets

Our code belongs to everyone. Thus, we strive to offer the most possible commented, documented, and exemplified code of all time. In Opytimizer, we present code snippets in an attempt to fulfill everyone's needs.

Applications

Additional Features

• create_optimization_checkpoints.py: How-to create checkpoints of optimization procedure;
• multiple_optimization_runnings.py: How-to invoke multiple optimizations using the same object;
• resume_optimization_from_file.py: How-to resume an optimization from a model saved in disk.

Multi-Objective

• multi_objective_optimization.py: How-to optimize multi-objective functions.

Single-Objective

• boolean_optimization.py: How-to optimize boolean functions;
• calculate_pareto_frontier.py: How-to calculate Pareto frontiers based on pre-defined points;
• constrained_standard_optimization.py: How-to optimize constrained single-objective functions;
• genetic_programming_optimization.py: How-to optimize functions with Genetic Programming;
• grid_search_optimization.py: How-to optimize functions with Grid Search;
• hyper_complex_optimization.py: How-to optimize functions with hypercomplex numbers;
• standard_optimization.py: How-to optimize single-objective functions.

Core

• create_agent.py: Agent class creation;
• create_function.py: Function class creation;
• create_node.py: Node class creation, which is used for the tree-based Search Space.

Functions

• create_constrained_function.py: Constrained single-objective function.

Multi-Objective Functions

• create_multi_objective_function.py: Multi-objective function;
• create_multi_objective_weighted_function.py: Weighted multi-objective function.

Integrations

• Learnergy: Energy-based machine learning algorithms;
• NALP: Natural Adversarial Language Processing architectures;
• OPFython: Our Optimum-Path forest implementation;
• PyTorch: One of the best tensor-based machine learning packages;
• Sklearn: The traditional machine learning package, known as Scikit-Learn;
• Tensorflow: Another renowed tensor-based machine learning package.

Math

• calculate_hypercomplex_numbers.py: A how-to guide in using hypercomplex numbers;
• general_purpose.py: General purpose functions implemented to assist other methods;
• generate_distributions.py: How-to generate distributions;
• generate_random_numbers.py: How-to generate random numbers.

Optimizers

Boolean

• create_bmrfo.py: Boolean Manta Ray Foraging Optimization;
• create_bpso.py: Boolean Particle Swarm Optimization;
• create_udma.py: Univariate Marginal Distribution Algorithm.

Evolutionary

• create_bsa.py: Backtracking Search Optimization Algorithm;
• create_de.py: Differential Evolution;
• create_ep.py: Evolutionary Programming;
• create_es.py: Evolution Strategies;
• create_foa.py: Forest Optimization Algorithm;
• create_ga.py: Genetic Algorithm;
• create_ghs.py: Global-Best Harmony Search;
• create_goghs.py: Generalized Opposition Global-Best Harmony Search;
• create_gp.py: Genetic Programming;
• create_hs.py: Harmony Search;
• create_ihs.py: Improved Harmony Search;
• create_iwo.py: Invasive Weed Optimization;
• create_nghs.py: Novel Global Harmony Search;
• create_rra.py: Runner-Root Algorithm;
• create_sghs.py: Self-Adaptive Global-Best Harmony Search.

Misc

• create_aoa.py: Arithmetic Optimization Algorithm;
• create_cem.py: Cross-Entropy Method;
• create_doa.py: Darcy Optimization Algorithm;
• create_gs.py: Grid Search;
• create_hc.py: Hill Climbing;
• create_nds.py: Non-Dominated Sorting.

Population

• create_aeo.py: Artificial Ecosystem-based Optimization;
• create_ao.py: Aquila Optimizer;
• create_coa.py: Coyote Optimization Algorithm;
• create_epo.py: Emperor Penguin Optimizer;
• create_gco.py: Germinal Center Optimization;
• create_gwo.py: Grey Wolf Optimizer;
• create_hho.py: Harris Hawks Optimization;
• create_loa.py: Lion Optimization Algorithm;
• create_osa.py: Owl Search Algorithm;
• create_ppa.py: Parasitism-Predation Algorithm;
• create_pvs.py: Passing Vehicle Search;
• create_rfo.py: Red Fox Optimization.

Science

• create_aig.py: Algorithm of the Innovative Gunner;
• create_aso.py: Atom Search Optimization;
• create_bh.py: Black Hole;
• create_efo.py: Eletromagnetic Field Optimization;
• create_eo.py: Equilibrium Optimizer;
• create_esa.py: Electro-Search Algorithm;
• create_gsa.py: Gravitational Search Algorithm;
• create_hgso.py: Henry Gas Solubility Optimization;
• create_lsa.py: Lightning Search Algorithm;
• create_moa.py: Magnetic Optimization Algorithm;
• create_mvo.py: Multi-Verse Optimizer;
• create_sa.py: Simulated Annealing;
• create_teo.py: Thermal Exchange Optimization;
• create_two.py: Tug Of War Optimization;
• create_wca.py: Water Cycle Algorithm;
• create_wdo.py: Wind Driven Optimization;
• create_weo.py: Water Evaporation Optimization;
• create_wwo.py: Water Wave Optimization.

Social

• create_bso.py: Brain Storm Optimization;
• create_ci.py: Cohort Intelligence;
• create_isa.py: Interactive Search Algorithm;
• create_mvpa.py: Most Valuable Player Algorithm;
• create_qsa.py: Queuing Search Algorithm;
• create_ssd.py: Social Ski-Driver.

Swarm

• create_abc.py: Artificial Bee Colony;
• create_aiwpso.py: Adaptive Inertia Weight Particle Swarm Optimization;
• create_af.py: Artificial Flora;
• create_ba.py: Bat Algorithm;
• create_boa.py: Butterfly Optimization Algorithm;
• create_bwo.py: Black Widow Optimization;
• create_cs.py: Cuckoo Search;
• create_csa.py: Crow Search Algorithm;
• create_eho.py: Elephant Herding Optimization;
• create_fa.py: Firefly Algorithm;
• create_ffoa.py: Fruit-Fly Optimization Algorithm;
• create_fpa.py: Flower Pollination Algorithm;
• create_fso.py: Flying Squirrel Optimizer;
• create_goa.py: Grasshopper Optimization Algorithm;
• create_js.py: Jellyfish Search;
• create_kh.py: Krill Herd;
• create_mfo.py: Moth-Flame Optimization;
• create_mrfo.py: Manta Ray Foraging Optimization;
• create_nbjs.py: Non-Bounded Jellyfish Search;
• create_pio.py: Pigeon-Inspired Optimization;
• create_pso.py: Particle Swarm Optimization;
• create_rpso.py: Relativistic Particle Swarm Optimization;
• create_savpso.py: Self-Adaptive Velocity Particle Swarm Optimization;
• create_sbo.py: Satin Bowerbird Optimization;
• create_sca.py: Sine Cosine Algorithm;
• create_sfo.py: Sailfish Optimizer;
• create_sos.py: Symbiotic Organisms Search;
• create_ssa.py: Salp Swarm Algorithm;
• create_sso.py: Simplified Swarm Optimization;
• create_stoa.py: Sooty Tern Optimization Algorithm;
• create_vpso.py: Vertical Particle Swarm Optimization;
• create_woa.py: Whale Optimization Algorithm.

Spaces

• create_boolean_space.py: A boolean-based Search Space used to guide boolean-based objective functions;
• create_grid_space.py: A custom Search Space based on a grid, which uses only one agent to evaluate all windows inside a desired grid;
• create_hyper_space.py: Hypercomplex numbers? The hyper Search Space offers the possibility of working with higher dimensions;
• create_pareto_space.py: A pareto-based Search Space used to calculate the Pareto frontier of pre-defined data points;
• create_search_space.py: The traditional creation of the Search Space, which will be used for almost all techniques;
• create_tree_space.py: A tree-based Search Space that guides the Genetic Programming.

Utils

• custom_callbacks.py: Defines customizable callbacks;
• inspect_optimization_history: Inspects the optimization history;
• interact_with_history.py: Explains how to interact with the optimization history object.

Visualization

• convergence_plots.py: A helper that will guide you in loading a History object and creating a convergence plot of it;
• function_surface_plotting.py: Creates a 3-D plot from an objective function.