The following arguments are supported:
- you can pass the number of iterations as argument (
max_iter
) tosolve_with_fstpso()
. Default is 100 iterations; - you can use a non-uniform initialization of the particles using the optional argument creation_method of
solve_with_pso()
. For example:solve_with_fstpso(creation_method={'name': 'logarithmic'})
; - you can also override the heuristics for the swarm size, by explicitly setting the number of particles with the method
set_swarm_size()
.
You can disable the fuzzy rules for the social factor, cognitive factor, inertia weight, minimum velocity, and maximum velocity by using the following methods before calling solve_with_fstpso()
:
disable_fuzzyrule_social()
;disable_fuzzyrule_cognitive()
;disable_fuzzyrule_inertia()
;disable_fuzzyrule_minvelocity()
;disable_fuzzyrule_maxvelocity()
.
You can provide a list/array of guesses to the initial population using the optional argument initial_guess_list
of the solve_with_fstpso()
method. Please note that the number of guesses must be smaller than the swarm size.