Membrane computing is a branch of natural computing which has been extended to solve various optimization problems. A hybrid mode membrane-computing-based algorithm (HMMCA) is proposed in this paper to solve complex unconstrained optimization problems with continuous variables. The algorithmic framework of HMMCA translates from its distributed cell-like membrane structure and communication rule. A non-deterministic evolutionary programming method and two computational rules are applied to enhance the computational performance. In a numerical simulation, 12 benchmark test functions with different variables are used to verify the algorithmic performance. The test results and comparison with three other algorithms illustrate its effectiveness and superiority. Moreover, a case study on a proton exchange membrane fuel cell (PEMFC) system parameter optimization problem is applied to validate its practicability. The results of the simulation and comparison with seven other algorithms demonstrate its practicability.

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