A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling

For the purpose of applying membrane computing as a global optimization technique, a bio-inspired algorithm based on membrane computing (BIAMC) is proposed to solve both constrained and unconstrained problems. The membrane structure used in BIAMC is a network of membranes that is inspired by the Golgi apparatus. In the process of approaching to the optimum solution, the objects containing a tentative solution are evolved by the rewriting rule in the parallel identical membranes and synthesized by the novel rules of target indication, transition and abstraction in the membrane of quasi-Golgi. The information transfers according to directions defined by the communication rule. Eight well-known unconstrained and constrained functions are used for performance testing. Then we apply the proposed algorithm with two schemes to solving a typical nonlinear optimization of gasoline blending and scheduling problem. The results show that the proposed approach can find optimal or close-to-optimal solutions efficiently.