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SpyOpt

SpyOpt is a C++ implementation of the metaheuristic spy_algorithm [1]. It can search for global optimal solutions for arbitrary non-convex functions.

SpyOpt はメタヒューリスティクスである spy_algorithm [1] の C++ 実装です。 任意の非凸関数に対し、大域最適解を探索することができます。

Demo

Ackley Function

Eggholder Function

Requirement

  • A compiler that supports C++17
  • yaml-cpp library

Installation

  1. Install yaml-cpp:

    On Ubuntu:

    sudo apt update
sudo apt install libyaml-cpp-dev

  2. Clone the repository:

    git clone [email protected]:KentaKato/SpyOpt.git
cd SpyOpt

  3. Create a build directory:

    mkdir build
cd build

  4. Run CMake and build:

    cmake ..
make

This will produce the executable file spyopt in the build directory.

Usage

  1. Execute Optimization

    After building, you can run the spyopt executable:

    ./spyopt

    The results will be saved in results/history.yaml.

  2. Animation of Otimization History

    cd SpyOpt
python3 scripts/create_history_animation.py

    The gif file will be saved in current directory.

Configuration

How to Modify Search Parameters

Settings can be adjusted via resources/config.yaml.

num_agents:    50  # Number of agents. Should be greater than (num_high_rank + num_mid_rank)
num_high_rank: 10  # Number of high rank agents
num_mid_rank:  25  # Number of mid rank agents
num_iterations: 50 # Number of iterations
swing_factor: 1
objective_function: Ackley # Booth, Eggholder, Ackley

How to Customize the Objective Function

  1. Implement it as following:

    double your_custom_function(const std::vector<double> &pos)
{
    ...
    return ...;
}

  2. Set the search space in resources/config.yaml.

    lower_bounds: [-5., -5.]
upper_bounds: [5., 5.]

  3. Pass the custom function as an argument to the SpyOpt`` constructor and execute the optimize()` method.

    Minimum Code Example:

    #include <iostream>
#include "SpyOpt/spy_opt.h"
#include "SpyOpt/config_parser.h"
#include "SpyOpt/objective_functions.h" // Assuming you've added your custom function in this header

int main() {
    // Define a simple custom objective function
    auto custom_function = [](const std::vector<double> &pos) -> double
    {
        return pos[0] * pos[0] + pos[1] * pos[1];
    };

    Config config;
    if (!parseConfig("resources/config.yaml", config))
    {
        std::cerr << "[Error] Failed to parse config!" << std::endl;
        return -1;
    }

    SpyOpt spy_alg(config, custom_function);
    spy_alg.optimize();

    const auto [fitness, pos] = spy_alg.getBestFitness();
    std::cout << "Best solution:" << std::endl;
    spy_alg.printBestAgent();
    spy_alg.dumpHistory("results/history.csv");

    return 0;
}

Reference

[1] Pambudi, Dhidhi, and Masaki Kawamura. "Novel metaheuristic: spy algorithm." IEICE TRANSACTIONS on Information and Systems 105.2 (2022): 309-319.