Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic...Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.展开更多
An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector w...An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.展开更多
考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度...考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。展开更多
针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提...针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。展开更多
文摘Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.
基金supported by the National Natural Science Foundation of China (60632050)National Basic Research Program of Jiangsu Province University (08KJB520003)
文摘An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.
文摘考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。
文摘针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。
