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针对化学反应动力学机理模型复杂、非线性等特点,本文提出了1种基于黄金分割的改进遗传算法。该算法主要将整个遗传进程分成3个阶段,前2段配备不同的改善种群多样性的选择交叉算子,最后采用改进的“最佳保存策略”加速收敛,这样能够有效避免经典遗传算法中出现的计算效率较低,收敛迟缓,容易早熟等问题。本文改进的算法依托MATLAB软件平台对二元多峰Schaffer函数进行了仿真性能测试,最后将其成功地应用到了SO2氧化反应动力学参数优化案例中,并与文献中的方法进行对比,结果表明,其在收敛精度(接近5×10~(-5))与收敛效率方面效果更好,而且这种算法的应用不依赖于案例的具体领域,可用于解决化工系统中类似的参数估计问题。
Aiming at the complex and non-linear kinetic model of chemical reaction, an improved genetic algorithm based on golden section is proposed in this paper. The algorithm mainly divides the entire genetic process into three stages, the first two stages are equipped with different choice crossover operators to improve the diversity of the population, and finally the improved “optimal preservation strategy” is used to accelerate the convergence so that the classical genetic algorithm Appear in the calculation of low efficiency, slow convergence, easy to premature and other issues. The improved algorithm relies on the MATLAB software platform to simulate the bivariate multi-peak Schaffer function. Finally, it is successfully applied to the SO2 oxidation kinetic parameters optimization case, and compared with the literature, the results show that, It is more effective in terms of convergence accuracy (close to 5 × 10 -5) and convergence efficiency, and the application of this algorithm does not depend on specific areas of the case and can be used to solve similar parameter estimation problems in chemical systems.