欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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.
英文摘要：
In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.
同期刊论文项目
同项目期刊论文
Consensus analysis of continuous-time second-order multi-agent systems with nonuniform time-delays a
Minimizing neighborhood evidential decision error for feature evaluation and selection based on evid
Convenient T-S fuzzy model with enhanced performance using a novel swarm intelligent fuzzy clusterin
Sliding mode H-infinity control design for uncertain nonlinear stochastic state-delayed Markovian ju
Stabilization of interconnected nonlinear stochastic Markovian jump systems via dissipativity approa
A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fi
期刊信息
位置：