论文部分内容阅读
为了综合考虑锅炉燃烧优化问题中锅炉效率与NOx排放2个目标,提出了一种新的基于免疫细胞亚群的多目标优化算法ICSMOA.算法定义了亚群划分算子与免疫耐受算子,亚群划分可以很方便地表达偏好,免疫耐受则能保证解的分布性.ICSMOA的运行结果为一组Pareto最优解,而传统的加权法的运行结果为一个不能判断Pareto占优与否的解.与多次运行加权法获得的结果相比,所提算法的运行结果优于加权法.另外,运行ICS-MOA所获得的Pareto前沿不同于经典的多目标优化算法,它可以输出更多的满足决策者偏好的解,从而更适合于工业应用.
In order to comprehensively consider the two objectives of boiler efficiency and NOx emissions in the combustion optimization of a boiler, a new multi-objective optimization algorithm ICSMOA based on immune cell subpopulation is proposed.The algorithm defines sub-grouping operator and immune tolerance operator, Subgroups can be easily expressed preferences, immune tolerance can guarantee the solution of the distribution.ICSMOA results of the operation as a set of Pareto optimal solution, and the results of the traditional weighted method can not be judged as a dominant Pareto or not Compared with the results of multiple run-weighted method, the results of the proposed algorithm outperformed the weighted method.In addition, the Pareto front obtained by running ICS-MOA is different from the classical multi-objective optimization algorithm, which can output more More solutions to the preferences of policymakers, making them more suitable for industrial applications.