This repository implements a powerful meta-heuristic algorithm named the cuckoo optimization algorithm in C++ programming language. The implementation inspired by the original Matlab implementation and may not work as good as original implementation.

• Fully vectorized
• Support parallel running
• Real valued optimization, appropriate for solving many problems.

• armadillo
• mlpack
• C++14
• openmp (optional)

The COA class is defined as

COA(function<double(rowvec)> f, unsigned int n_par, double var_lo,double var_hi)

• f : A function which computes the cost value.
• n_par : The number of cost function arguments.
• var_lo, var_hi : The minimum and maximum values of each dimension.

The other parameters of COA can be set after constructing the class. See usage for more information.

• Implement a function which inputs a rowvec and returns a double value.

  double ackley2(rowvec x) {
      double result;
      size_t n = x.n_cols;
      double s1=0, s2=0;
      for (size_t j=0; j<n; j++){
          s1+=pow(x(j),2);
          s2+=cos(2*PI*x(j));
      }
      result = -20 * exp(-.2 * sqrt(1.0/n * s1)) - exp(1.0/n * s2)+ 20 + exp(1);
      return result;
  }
  

• Define necessary parameters

  unsigned int npar = 7;
  double var_lo = 0, var_hi = 1;
  

• Initialize the COA class

  COA coa(ackley2, npar, var_lo, var_hi);
  

• Set the optional parameters

  coa.num_of_cuckoos = 25;
  coa.max_num_of_cuckoos = 50;
  coa.motion_coeff = 20;
  coa.radius_coeff = 15;
  coa.max_iter = 30;
  coa.cuckoo_pop_variance = 0;
  coa.accuracy = 0;
  coa.verbose = false;
  

• Run the optimizer

  rowvec c = coa.run();
  cout << c;
  

• Compile the source code

  • with parallelization

    g++ -larmadillo -O3 -std=c++14 -fPIC -larmadillo -lmlpack -fopenmp main.cpp -o tmp/coa_parallel
    

  • without paralleization

    g++ -larmadillo -O3 -std=c++14 -fPIC -larmadillo -lmlpack main.cpp -o tmp/coa_serial
    

On the GNU/Linux operating systems, you can easily run build.sh file. This script compiles the main.cpp with previous section commands. The result on Intel Core i5 4200U with 4 logical cores (HT technology) for solving a simple integral equation is

start compiling...  
All files compiled successfully!  
serial time is 9.18 sec  
parallel time is 3.95 sec  
speedup = 2.32

• python bindings
• publish on pypi
• GPU support