# Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization

arXiv: https://arxiv.org/abs/1408.5332 · HTML: https://mlsanity.com/paper/1408.5332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2014.

## Built on by (most cited first, 2 of 2)

- [MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection](https://mlsanity.com/paper/2402.08982/) (2024)
- [A Nature-Inspired Feature Selection Approach based on Hypercomplex Information](https://mlsanity.com/paper/2101.05652/) (2021)

## Abstract (arXiv)

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.