The k-Means clustering algorithm is highly depends on the initial solution and is easy to trap into the local optimal.

Flower Pollination Algorithm is a novel approach for multi-objective optimization.

Discard pollen operator and crossover operator are applied to increase diversity of the population, and local searching ability is enhanced by using elite based mutation operator.

Compared with DE, CS, ABC, PSO, FPA and k-Means, the experiment results show that Flower Pollination Algorithm with Bee Pollinator has higher accuracy, higher level of stability, and the faster convergence speed.