Heuristic — nature-inspired metaheuristic
▶ Open interactive explainer →Flower Pollination Algorithm
| Alias | fpa, flower_pollination |
| Type | Heuristic — nature-inspired metaheuristic |
| Auto-seeds from | shuffle (random flowers) |
Description
Nature-inspired metaheuristic modelling the pollination process of flowering plants. Maintains a population of flowers (candidate tours). Each epoch, each flower applies either:
- Global pollination (probability
switch_prob): a Lévy-flight-scaled move toward the global best tour — analogous tox + γ·L·(g* − x)in the continuous version. - Local pollination (probability
1 − switch_prob): an ε-scaled displacement combining two randomly chosen flowers — analogous tox + ε·(x_j − x_k).
The switch probability controls the balance between exploitation (global) and exploration (local).
TSP-specific adaptations (deviations from Yang 2012):
| Adaptation | Reason |
|---|---|
| Global pollination → Lévy-scaled prefix of swap sequence toward gbest | Permutation analogue of the continuous update; preserves tour validity |
| Local pollination → ε-scaled prefix of swap diff between two random flowers | Permutation analogue of local cross-pollination |
switch_prob floored at 0.8 when mutation_probability < 0.01 | Prevents degeneration to 99.9 % local-only search under default CLI options |
Auto-expands to pipeline(shuffle, fpa).
procedure FlowerPollination(cities, n_flowers, p, epochs):
flowers ← initialize_flowers(n_flowers, cities)
gbest ← best_tour(flowers)
for epoch in 1..epochs:
for each flower i:
if random() < p:
step ← levy_flight()
candidate ← move_toward(flowers[i], gbest, step)
else:
j, k ← two random flower indices
ε ← random()
candidate ← local_crossover(flowers[i], flowers[j], flowers[k], ε)
if length(candidate) < length(flowers[i]):
flowers[i] ← candidate
gbest ← best_tour(flowers)
return gbestOptions
| Flag | Description | Default |
|---|---|---|
--epochs | Maximum iterations | 10 000 |
--n_nearest | Number of flowers (floored at 25) | 25 |
--mutation_probability | Switch probability (global vs local pollination) | 0.8 |
Usage
teeline solve fpa -i ./data/tsplib/berlin52.tsp
teeline solve flower_pollination -i ./data/tsplib/berlin52.tsp --epochs=500
teeline solve fpa -i ./data/tsplib/berlin52.tsp --n_nearest=50 --mutation_probability=0.8References
- Yang, X.-S. (2012) — "Flower Pollination Algorithm for Global Optimization", in Unconventional Computation and Natural Computation (UCNC 2012), LNCS 7445, pp. 240–249, Springer
- Flower pollination algorithm (Wikipedia)