The fitness-dependent optimizer (FDO) algorithm was recently introduced
in 2019. An improved FDO (IFDO) algorithm is presented in this work, and
this algorithm contributes considerably to refining the ability of the
original FDO to address complicated optimization problems. To improve
the FDO, the IFDO calculates the alignment and cohesion and then uses
these behaviors with the pace at which the FDO updates its position.
Moreover, in determining the weights, the FDO uses the weight factor (
), which is zero in most cases and one in only a few cases. Conversely,
the IFDO performs randomization in the [0-1] range and then
minimizes the range when a better fitness weight value is achieved. In
this work, the IFDO algorithm and its method of converging on the
optimal solution are demonstrated. Additionally, 19 classical standard
test function groups are utilized to test the IFDO, and then the FDO and
three other well-known algorithms, namely, the particle swarm algorithm
(PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are
selected to evaluate the IFDO results. Furthermore, the CECC06 2019
Competition, which is the set of IEEE Congress of Evolutionary
Computation benchmark test functions, is utilized to test the IFDO, and
then, the FDO and three recent algorithms, namely, the salp swarm
algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen
to gauge the IFDO results. The results show that IFDO is practical in
some cases, and its results are improved in most cases. Finally, to
prove the practicability of the IFDO, it is used in real-world
applications.
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