A collection of derivative-free optimization algorithms in Python and JavaScript
Install via pip with minimal dependencies:
pip install humpdayBasic usage for optimization problems:
from humpday import minimize
def objective(x):
return (x[0] - 2)**2 + (x[1] - 3)**2
result = minimize(objective, bounds=[(-5, 5), (-5, 5)], method='DifferentialEvolution')
print(f"Solution: {result.x}") # [2.0, 3.0]'PRIMA_UOBYQA', 'PRIMA_NEWUOA', 'PRIMA_BOBYQA',
'NelderMead', 'Powell', 'LBFGSB',
'DifferentialEvolution', 'ParticleSwarm', 'CMAEvolutionStrategy',
'EvolutionStrategy', 'GeneticAlgorithm', 'BayesianOpt',
'RandomSearch', 'AdaptiveRandomSearch', 'HillClimbing',
'CoordinateDescent', 'PatternSearch', 'SimulatedAnnealing',
'TabuSearch', 'HarmonySearch', 'FireflyAlgorithm',
'AntColonyOpt'
The following tools provide empirical evaluation and visualization of algorithm performance:
Comparative evaluation of optimizers on user-defined problems with statistical analysis.
Run Contest →Real-time visualization of optimization trajectories on three-dimensional objective surfaces.
View Visualization →Detailed documentation and interactive demos for each algorithm:
Model-based algorithms using quadratic approximations:
Workhorse non-derivative-free methods, ported to pure Python:
Population-based stochastic methods:
Acceptance-rule and memory-based search:
Lightweight baselines and direct-search workhorses: