  Multiobjective design optimization problems require multiobjective
optimization techniques to solve, and it is often very challenging to obtain
high-quality Pareto fronts accurately. In this paper, the recently developed
flower pollination algorithm (FPA) is extended to solve multiobjective
optimization problems. The proposed method is used to solve a set of
multobjective test functions and two bi-objective design benchmarks, and a
comparison of the proposed algorithm with other algorithms has been made, which
shows that FPA is efficient with a good convergence rate. Finally, the
importance for further parametric studies and theoretical analysis are
highlighted and discussed.
