Swarm Intelligence Search

Shuffled Frog Learping Algorighm

Solver name is “SFLA”.

class flopt.solvers.shuffled_frog_leaping_search.ShuffledFrogLeapingSearch

SFLA (Shuffled Frog Leaping Search) It has a incumbent solution anytime.

1. Generate new solutions as frogs at random.

2. Divide frog set into some memeplexes.

3. Improve each memeplex a certain number of times respectively.

4. Update best solution.

5. Redistribute memeplexes.

6. Repeat step3 to step5

Parameters:

• n_trial (int (default 1e10)) – number of memetic evolution

• max_step (float (default 1e10)) – maximum size of one step

• n_memetic_iter (int (default 100)) – number of evolution in each memeplex

• n_memeplex (int (default 5)) – number of memeplex

• n_frog_per_memeplex (int (default 10)) – number of frog per memeplex

• inc_flogsize (float) – Multiplier for increasing population size before each restart

Examples

import flopt

x = flopt.Variable("x", lowBound=-1, upBound=1, cat="Continuous")
y = flopt.Variable("y", lowBound=-1, upBound=1, cat="Continuous")

prob = flopt.Problem()
prob += 2*x*x + x*y + y*y + x + y

status, log = prob.solve(solver="SFLA", msg=True, timelimit=1)

print("obj =", flopt.Value(prob.obj))
print("x =", flopt.Value(x))
print("y =", flopt.Value(y))