Circular genetic operators based RNA genetic algorithm for modeling proton exchange membrane fuel cells

• Published in: International journal of hydrogen energy Vol. 39; no. 31; pp. 17779 - 17790
• Main Authors: Zhu, Qinqin, Wang, Ning, Zhang, Li
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 22.10.2014; Elsevier
• Subjects: Alternative fuels. Production and utilization; Applied sciences; Biological computing; Circular genetic operators; Energy; Estimates; Exact sciences and technology; Exchange; Fuels; Genetic algorithms; Genetics; Hydrogen; Mathematical models; Mutations; Operators; Parameter estimation; Proton exchange membrane fuel cell (PEMFC); Proton exchange membrane fuel cells; Ribonucleic acids; RNA genetic algorithm; Circular genetic operators; RNA genetic algorithm; Parameter estimation; Biological computing; Proton exchange membrane fuel cell (PEMFC); Genetic algorithm; Hydrogen; Modeling; Proton exchange membrane fuel cells; Fuel cell
• ISSN: 0360-3199; 1879-3487
• DOI: 10.1016/j.ijhydene.2014.07.081

Inspired by the biological RNA, a circular genetic operators based RNA genetic algorithm (cRNA-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the population diversity and avoid premature convergence, we design the novel genetic operator of the double-loop crossover operator. To allow the algorithm to jump out of local optima, the adaptive mutation probabilities are presented and the stem-loop mutation operator is adopted with the other mutation operators. The simulated annealing method is also incorporated into the cRNA-GA to improve local search ability. Performance tests conducted on some typical benchmark functions have witnessed the validity of cRNA-GA. The cRNA-GA is also applied to estimate the parameters of the PEMFC model and the satisfactory results have shown its effectiveness. [Display omitted] •The circular genetic operators based RNA genetic algorithm (cRNA-GA) is proposed.•Inspired by the biological RNA, the double-loop crossover operator is design.•The adaptive mutation probabilities and the implement of cRNA-GA are presented.•The efficiency of cRNA-GA is demonstrated for PEMFC model parameter estimation.