  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.
