  Optimization techniques, used to get the optimal solution in search spaces,
have not solved the time-consuming problem. The objective of this study is to
tackle the sequential processing problem in Monkey Algorithm and simulating the
natural parallel behavior of monkeys. Therefore, a P system with active
membranes is constructed by providing a codification for Monkey Algorithm
within the context of a cell-like P system, defining accordingly the elements
of the model - membrane structure, objects, rules and the behavior of it. The
proposed algorithm has modeled the natural behavior of climb process using
separate membranes, rather than the original algorithm. Moreover, it introduced
the membrane migration process to select the best solution and the time stamp
was added as an additional stopping criterion to control the timing of the
algorithm. The results indicate a substantial solution for the time consumption
problem, significant representation of the natural behavior of monkeys, and
considerable chance to reach the best solution in the context of
meta-heuristics purpose. In addition, experiments use the commonly used
benchmark functions to test the performance of the algorithm as well as the
expected time of the proposed P Monkey optimization algorithm and the
traditional Monkey Algorithm running on population size. The unit times are
calculated based on the complexity of algorithms, where P Monkey takes a time
unit to fire rule(s) over a population size n; as soon as, Monkey Algorithm
takes a time unit to run a step every mathematical equation over a population
size.
