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Bayesian Multi-Scale Optimistic Optimization

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:sparkles: BaMSOO :sparkles:

Bayesian Multi-Scale Optimistic Optimization This is a simple MATLAB implementation of BaMSOO, described in the paper Bayesian Multi-Scale Optimistic Optimization by Wang et al.. The paper can be found here

The repository has four files:


To run a simple demo, fire MATLAB and run the following command:


Optimization Problem:

To solve an optimization(minimization) problem, we need to specify the function, target function value, the dimensions, the min and max range of the search space, and the evaluation budget. An example is provided here:

% Specify the problem
>>func = @(x) sum((x-0.6).^2);
>>dimension = 2;
>>maxRange = 1;
>>minRange = -1;
>>numEvaluations = 1000;
>>ftarget = 1e-5;
% Solve the problem
>>[yBest, xBest, nodes ]= BaMSOO(func, ftarget, dimension, maxRange, minRange, numEvaluations);
>>fprintf('optimal f-value is %f\n',yBest)

Practical Issues:

Parameter settings

Parameter settings for other partitioning based algorithms can be found in AlgorithmParameters


If you write a scientific paper describing research that made use of this code, please cite the following paper:

    author    = {Abdullah Al-Dujaili and S. Suresh},
    title     = {Analysis of the Bayesian Multi-Scale Optimistic Optimization on the Noiseless BBOB Testbed},
    journal   = {},
    year      = 2015,
    pages     = {},
    url       = ""