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<p>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.</p>
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