Nature-inspired algorithms have become popular due to their efficiency in finding close-to-optimal solutions for complex optimization problems. The development of new metaheuristic algorithms and variant versions of existing ones is a hot research topic. The Symbiotic Organism Search (SOS) is a nature-inspired, population-based optimization metaheuristic that simulates symbiotic relationships observed in nature. It has been successfully applied to solve different optimization problems. In this paper, we propose a modified version of SOS, named MSOS, that adds an allocation step to the original SOS to select organisms for each phase of the algorithm in such a way that the probability of generating improvements in the current solutions is increased. A numerical example considering the design of an adaptive PID controller is presented to illustrate the application of the proposed algorithm. The experiments show that the proposed MSOS outperformed the original SOS for the optimization problem under consideration.