  Child drawing development optimization (CDDO) is a recent example of a
metaheuristic algorithm. The motive for inventing this method is children's
learning behavior and cognitive development, with the golden ratio employed to
optimize their artwork's aesthetic value. Unfortunately, CDDO suffers from low
performance in the exploration phase, and the local best solution stagnates.
Harmony search (HS) is a highly competitive algorithm relative to other
prevalent metaheuristic algorithms, as its exploration phase performance on
unimodal benchmark functions is outstanding. Thus, to avoid these issues, we
present CDDOHS, a hybridization of both standards of CDDO and HS. The
hybridized model proposed consists of two phases. Initially, the pattern size
(PS) is relocated to the algorithm's core and the initial pattern size is set
to 80 % of the total population size. Second, the standard harmony search (HS)
is added to the pattern size (PS) for the exploration phase to enhance and
update the solution after each iteration. Experiments are evaluated using two
distinct standard benchmark functions, known as classical test functions,
including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the
suggested CDDOHS is compared to CDDO, HS, and six other widely used algorithms.
Using the Wilcoxon ranksum test, the results indicate that CDDOHS beats
alternative algorithms.
