This is a general-purpose mathematical optimization library for C++. By applying this library, you can solve single- and multi-variable optimizations, constrained and unconstrained problems and even non-convex problems.

This solution is based on combination of particle swarm optimization and delta potential well from quantum physics. The mathematical optimizer inspired by quantum mechanics is a new way to enhance classical optimization techniques to have higher chance in finding of global minima, while the C++ implementation is a great tool for faster running.

I wanted to provide a reliable tool that applies state-of-the-art approaches and it is also fast and easily integrable into other programming environments as well. For example, wrapping C++ code into .Net environment or into Python, is also possible and helps a lot to improve the running speed of your projects as well.

• Delta potential well is applied to calculate movements of particles
• Velocity parameter is removed
• Linearly rebalancing explorative and exploiting ratios to controll the importance of the local and global minima for the swarm
• There re only two required input parameters from the user:
  • swarm size
  • dimension of the problem
• Thus, no apriori knowledge is necessary to find better solutions. This solver is ready to manage your problem without any fine-tuning.

This library requires GCC v9+ to run.

After cloning the repo, you can run the tester:

cd quantum-particle-swarm-optimization
g++ -O -Wall tester.cpp -o tester
./tester

You can expect an output like this:

Goldstein price function benchmarking
Swarm size: 300
Total iterations: 1000
Global Best: 3
Griewank function benchmarking
Swarm size: 200
Total iterations: 1000
Global Best: 0
Schwefel function benchmarking
Swarm size: 200
Total iterations: 2000
Global Best: 947.507

Apply this header in your C++ project:

#include "qpso.h"

Then you can call the Swarm Optimizer:

QPSOptimiser::Swarm testSwarm(200, 30, foo); // swarmSize = 200; dimensions = 30; problem to solve = foo
testSwarm.solve(1000); // Iteration number is 1000
testSwarm.print_results(true); // If argument is True, then it will show the calculated arguments' values

Want to contribute? Great!

Please send a message to Roland Szenczi and we can discuss.

MIT