Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
This work addresses multiobjective design optimization problems for researchers and engineers, but it appears incremental as it extends an existing algorithm to a new application.
The authors tackled the challenge of obtaining high-quality Pareto fronts in multiobjective optimization by extending the flower pollination algorithm (FPA) to solve such problems, showing that FPA is efficient with a good convergence rate in tests on benchmark functions.
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.