BlackBoxOptim.jl

BlackBoxOptim is a is a Julia package implementing (Meta-)heuristic/stochastic algorithms that do not require for the optimized function to be differentiable.

Installation: OptimizationBBO.jl

To use this package, install the OptimizationBBO package:

import Pkg; Pkg.add("OptimizationBBO")

Global Optimizers

Without Constraint Equations

The algorithms in BlackBoxOptim are performing global optimization on problems without constraint equations. However, lower and upper constraints set by lb and ub in the OptimizationProblem are required.

A BlackBoxOptim algorithm is called by BBO_ prefix followed by the algorithm name:

  • Natural Evolution Strategies:
    • Separable NES: BBO_separable_nes()
    • Exponential NES: BBO_xnes()
    • Distance-weighted Exponential NES: BBO_dxnes()
  • Differential Evolution optimizers, 5 different:
    • Adaptive DE/rand/1/bin: BBO_adaptive_de_rand_1_bin()
    • Adaptive DE/rand/1/bin with radius limited sampling: BBO_adaptive_de_rand_1_bin_radiuslimited()
    • DE/rand/1/bin: BBO_de_rand_1_bin()
    • DE/rand/1/bin with radius limited sampling (a type of trivial geography): BBO_de_rand_1_bin_radiuslimited()
    • DE/rand/2/bin: de_rand_2_bin()
    • DE/rand/2/bin with radius limited sampling (a type of trivial geography): BBO_de_rand_2_bin_radiuslimited()
  • Direct search:
    • Generating set search:
      • Compass/coordinate search: BBO_generating_set_search()
      • Direct search through probabilistic descent: BBO_probabilistic_descent()
  • Resampling Memetic Searchers:
    • Resampling Memetic Search (RS): BBO_resampling_memetic_search()
    • Resampling Inheritance Memetic Search (RIS): BBO_resampling_inheritance_memetic_search()
  • Stochastic Approximation:
    • Simultaneous Perturbation Stochastic Approximation (SPSA): BBO_simultaneous_perturbation_stochastic_approximation()
  • RandomSearch (to compare to): BBO_random_search()

The recommended optimizer is BBO_adaptive_de_rand_1_bin_radiuslimited()

The currently available algorithms are listed here

Example

The Rosenbrock function can optimized using the BBO_adaptive_de_rand_1_bin_radiuslimited() as follows:

rosenbrock(x, p) =  (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
x0 = zeros(2)
p  = [1.0, 100.0]
f = OptimizationFunction(rosenbrock)
prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters=100000, maxtime=1000.0)