چکیده (انگلیسی):

Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize
the structure of membrane computing to improve intelligent algorithms. These studies have applied
communication rules in membrane models to facilitate information exchange between membranes, thereby
improving the performance of those algorithms. However, parallel membrane computing has not yet been
considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous
studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an
appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be
executed over different cores in a parallel manner. The proposed algorithm can be executed over different
cores and uses multi-core processing to implement parallel membrane computation. Simulation with a
Colville minimization problem shows that the membrane computing inspired genetic algorithm has
improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.