This paper proposes Komodo Mlipir Algorithm (KMA) as a new metaheuristic optimizer. It is inspired
by two phenomena: the behavior of Komodo dragons living in the East Nusa Tenggara, Indonesia,
and the Javanese gait named mlipir. Adopted the foraging and reproduction of Komodo dragons, the
population of a few Komodo individuals (candidate solutions) in KMA are split into three groups
based on their qualities: big males, female, and small males. First, the high-quality big males do
a novel movement called high-exploitation low-exploration to produce better solutions. Next, the
middle-quality female generates a better solution by either mating the highest-quality big male
(exploitation) or doing parthenogenesis (exploration). Finally, the low-quality small males diversify
candidate solutions using a novel movement called mlipir (a Javanese term defined as a walk on
the side of the road to reach a particular destination safely), which is implemented by following
the big males in a part of their dimensions. A self-adaptation of the population is also proposed
to control the exploitation–exploration balance. An examination using the well-documented twentythree benchmark functions shows that KMA outperforms the recent metaheuristic algorithms. Besides,
it provides high scalability to optimize thousand-dimensional functions. The source code of KMA is
publicly available