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What is FST-PSO?

FST-PSO is a settings-free version of the global optimization algorithm known as Particle Swarm Optimization. FST-PSO does not need any user settings to work, because particles leverage fuzzy logic to adapt their behavior to the fitness landscape.

If you find FST-PSO useful for your research, please cite it as:

Nobile, Cazzaniga, Besozzi, Colombo, Mauri, Pasi, “Fuzzy Self-Tuning PSO: A Settings-Free Algorithm for Global Optimization”, Swarm & Evolutionary Computation, 2017 (doi:10.1016/j.swevo.2017.09.001)

Why FST-PSO?

FST-PSO was specifically design to be 1) extremely easy to use (no settings!) and 2) more effective than normal PSO. Thus, the API is extremely simple and straightforward:

• create a FuzzyPSO class;
• specify the search space with its set_search_space() method;
• specify a fitness function with the set_fitness() method. Please note that the argument of this method must be a function which receives the position of a particle as argument and returns its fitness value as a real number;
• launch the optimization with the solve_with_fstpso() method.

Please note that you must specify the search space before setting the fitness function, because the set_fitness() method is designed to automatically test the proper functioning of the fitness function.

Are there any optional settings?

Yes. You can pass the number of iterations as argument (max_iter) to solve_with_fstpso().

Can I distribute the fitness evaluations?

Yes. In alternative to the set_fitness() method, you can use the set_parallel_fitness() method. In this case, you must specify as argument a function which receives the whole population and must return the vector of the fitness values for all particles. Please note that the position of a particle is contained in the field .X.