An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells
An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells
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DOI：
10.1016/j.ijhydene.2012.10.026
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发表时间：
2013-01
影响因子：
7.2
通讯作者：
Li Zhang;Ning Wang
中科院分区：
文献类型：
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作者：
Li Zhang;Ning Wang
准确的数学模型是燃料电池动力系统仿真和设计的关键。针对质子交换膜燃料电池(PEMFC)模型参数估计问题，借鉴生物RNA机理，提出了一种自适应RNA遗传算法(ARNA-GA)。ARNA-GA使用RNA链来表示潜在解，并设计了新的遗传算子来提高全局搜索能力。为了保持种群多样性，避免早熟收敛，提出了基于相异系数的自适应遗传策略，允许算法动态选择交叉操作或变异操作执行。对一些高维基准函数进行了数值实验。结果表明，ARNA-GA具有较强的搜索能力和较高的解质量。最后，将该方法应用于质子交换膜燃料电池模型的参数估计，得到了满意的结果。
The accurate mathematical model is the key issue to simulation and design of the fuel cell power systems. Aiming at estimating the proton exchange membrane fuel cell (PEMFC) model parameters, an adaptive RNA genetic algorithm (ARNA-GA) which is inspired by the mechanism of biological RNA is proposed. The ARNA-GA uses the RNA strands to represent the potential solutions and new genetic operators are designed for improving the global searching ability. In order to maintain the population diversity and avoid premature convergence, on the basis of the dissimilarity coefficient, the adaptive genetic strategy that allows the algorithm dynamically select crossover operation or mutation operation to execute is proposed. Numerical experiments have been conducted on some benchmark functions with high dimensions. The results indicate that ARNA-GA has better search capability and a higher quality of solutions. Finally, the proposed approach has been applied for the parameter estimation of PEMFC model and the satisfactory results are reached.