Accelerated execution of P systems with active membranes to solve the N-queens problem

Abstract

The N-queens problem has attracted increasing attention because of its potential applications in different areas, such as parallel memory storage approaches, image processing, and physical and chemical studies. Local search is a powerful method for solving real problems, such as the N-queens problem. Recently, models of P systems with active membranes have been used for local search to solve the N-queens problem. However, there have been insufficient studies of the parallelism of the P-system models with active membranes. In addition, the active membrane systems defined for N queens have several individual membranes that contain one object and no internal rules in each membrane, as well as several communication rules among membranes, which reduce the execution speed. In this study, a new P system model with active membranes is defined for solving the N-queens problem, and multi-core simulation of the proposed membrane system allows the execution of alternative computations in parallel, thus reducing the average time for finding a successful computation. The speed of the proposed model was compared with previous models that used P systems with active membranes for local search. The model contains two membranes, but the inclusion of several objects and rules within each membrane increases the parallelism and performance. This model reduces the number of communication rules required among membranes, and increases the execution speed. This model also increases the parallelism of previous P systems with active membranes when several rules evolve concurrently and more than one queen is exchanged during each step to reach a solution. Multi-core processing has been used to decrease the probability of restarting the P systems and to decrease processing time by distributing the processing of the active membrane on the multi-core. The speed of the proposed model when solving N=200 queens was almost 1000 times faster than previous methods.