  Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that
mimics the reproduction behavior of the bee swarm in finding better hives. This
algorithm is similar to Particle Swarm Optimization (PSO) but it works
differently. The algorithm is very powerful and has better results compared to
other common metaheuristic algorithms. This paper aims at improving the
performance of FDO, thus, the chaotic theory is used inside FDO to propose
Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of
them are performing well to avoid local optima and finding global optima. New
technic is used to conduct population in specific limitation since FDO technic
has a problem to amend population. The proposed CFDO is evaluated by using 10
benchmark functions from CEC2019. Finally, the results show that the ability of
CFDO is improved. Singer map has a great impact on improving CFDO while the
Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO.
Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO
is applied to classical engineering problems, such as pressure vessel design
and the result shows that CFDO can handle the problem better than WOA, GWO,
FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem
and then compared to the original FDO. The results prove that CFDO has better
capability to solve the problem.
