Flower pollination algorithm: A novel approach for multiobjective optimization

By Xin-She Yang, Mehmet Karamanoglu, Xingshi He. arXiv.org, 2014

594 citations recorded by the source corpus.

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 article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/f1b7d0f412f1d3e4b17852ccb95ea48f43654f13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xianbing Meng
2. Lihu Lu
3. Yu Liu
4. Xiao Zhi Gao
5. Hengzhen Zhang

In Computational Theory and Mathematics

1. Xianbing Meng
2. Yu Liu
3. Hengzhen Zhang

Citations in the corpus

• The Arithmetic Optimization Algorithm (2021, unknown): “…Algorithm (GA) [32] • Particle Swarm Optimization (PSO) [33] • Biogeography-based Optimization (BBO) [34] • Flower Pollination Algorithm (FPA) [35] • Grey Wolf Optimizer (GWO) [36] • Bat Algorithm (BAT) [37] • Firefly Algorithm (FA) [38] • Cuckoo Search Algorithm (CS) [39] • Moth-Flame…”
• A new bio-inspired optimisation algorithm: Bird Swarm Algorithm (2016, methodology): “Inspired by the plant, Invasive Weed Optimization (Mehrabian & Lucas, 2006) and Flower Pollination Algorithm (Yang, Karamanoglu, & He, 2014) were designed.”