“…Furthermore, there are many recent metaheuristic algorithms that have been stimulated by alive creatures' behaviors, such as the grey wolf optimizer (GWO), which was stimulated by the hierarchy of guidance and hunting [7]; the whale optimization algorithm (WOA), which was stimulated by producing spiral bubbles around a school of fish [8]; the salp swarm algorithm (SSA), which was stimulated by the teeming of salps to track food [9]; Harris hawk optimization (HHO), which was stimulated by the teeming work of many hawks to attack prey [10]; the mantis search algorithm (MSA), which was inspired by the foraging process of mantises [11]; the nutcracker optimization algorithm (NOA), which was stimulated by the seasonal deeds of nutcrackers in finding, storing, and memorizing food [12]; the Aquila optimizer (AO), which was stimulated by the hunting style of Aquila [13]; the black widow optimizer (BWO), which was stimulated by the mating and flesh-eating of black widow spiders [14]; and the Tunicate swarm algorithm (TSA), which was stimulated by the swarming manners of tunicates in tracking food [15]. Consequently, many algorithms are stimulated by the conduct of living creatures, for example, dolphins [16], white sharks [17], vultures [18], orcas [19], starlings [20], rabbits [21], frogs [22], butterflies [23], hyenas [24], reptiles [25], coati [26], leopards [27], and eagles [28].…”