# Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process

arXiv: https://arxiv.org/abs/1904.05226 · HTML: https://mlsanity.com/paper/1904.05226/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond](https://mlsanity.com/paper/2307.10420/) (2023)
- [The Fifteen Puzzle- A New Approach through Hybridizing Three Heuristics Methods](https://mlsanity.com/paper/2302.02985/) (2023)
- [Fitness Dependent Optimizer with Neural Networks for COVID-19 patients](https://mlsanity.com/paper/2302.02986/) (2023)
- [A New K means Grey Wolf Algorithm for Engineering Problems](https://mlsanity.com/paper/2103.05760/) (2021)
- [Artificial Liver Classifier: A New Alternative to Conventional Machine Learning Models](https://mlsanity.com/paper/2501.08074/) (2025)
- [An Improved FOX Optimization Algorithm Using Adaptive Exploration and Exploitation for Global Optimization](https://mlsanity.com/paper/2504.09574/) (2025)

## Abstract (arXiv)

In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The bee swarming reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. It is worth mentioning that FDO is considered a particle swarm optimization (PSO)-based algorithm that updates the search agent position by adding velocity (pace). However, FDO calculates velocity differently; it uses the problem fitness function value to produce weights, and these weights guide the search agents during both the exploration and exploitation phases. Throughout the paper, the FDO algorithm is presented, and the motivation behind the idea is explained. Moreover, FDO is tested on a group of 19 classical benchmark test functions, and the results are compared with three well-known algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA), additionally, FDO is tested on IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) [1]. The results are compared with three modern algorithms: (DA), the whale optimization algorithm (WOA), and the salp swarm algorithm (SSA). The FDO results show better performance in most cases and comparative results in other cases. Furthermore, the results are statistically tested with the Wilcoxon rank-sum test to show the significance of the results. Likewise, FDO stability in both the exploration and exploitation phases is verified and performance-proofed using different standard measurements. Finally, FDO is applied to real-world applications as evidence of its feasibility.