Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

TL;DR

This paper extends the flower pollination algorithm to multiobjective optimization, demonstrating its efficiency and good convergence on test functions and benchmarks, with discussions on future parametric and theoretical studies.

Contribution

The paper introduces a multiobjective version of the flower pollination algorithm, showing its effectiveness compared to other algorithms in solving complex optimization problems.

Findings

FPA achieves good convergence rates on test functions.

FPA performs well on bi-objective design benchmarks.

The paper highlights the need for further parametric and theoretical analysis.

Abstract

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.