多目标资源调查站位优化设计——以渤海为例

本研究以渤海为研究区域,通过数值模拟,分析了种类出现率(将基于渤海调查的17个主要渔业种类分为3类:Ⅰ类出现率≥70%,Ⅱ类出现率50%～70%,Ⅲ类出现率50%)和栖息水层对单种类资源量相对误差(Relative error, REE)的影响,不同调查站位数量(48和60)对定点采样与分层随机采样分析结果的影响,并优化了渤海多目标渔业资源调查的设计方案。结果显示,Ⅰ类中5种资源量REE在20%以内,Ⅱ类中3种资源量REE在30%以内,Ⅲ类中6种资源量REE在35%以内,即单种资源量评估值随种类出现率下降,相对误差变大;种类的栖息水层对种类资源量REE无明显影响。定点采样评估值随站位数量减少,精度下降(鱼类、虾类、蟹类、头足类的资源量指数和Margalef丰富度指数的REE分别增加了1.1%、2.5%、8.4%、4.4%和3.3%);分层随机采样可弥补站位数减少带来的精度下降,如站位数为48的分层随机采样获得的鱼类资源量指数评估精度(REE为4.6%)高于站位数为60的定点采样的精度(REE为7.7%),有助于减少调查成本和保护资源量低的种类。然而,每种采样方法并不能完全满足多目标最优,不同站位分配方案影响分层随机采样的精度,按照抽样费用最优准则设置站位,可获得精确度较高的鱼类、虾类、蟹类、头足类及黄鲫(Setipinnataty)、口虾蛄(Oratosquillaoratoria)、日本枪乌贼(Loligojaponica)、鳀鱼(Engraulis japonicus)、叫姑鱼(Johniusgrypotus)、泥脚隆背蟹(Carcinoplaxvestita)、中国对虾(Fenneropenaeus chinensis)等主要种类资源量评估结果,可作为渤海多目标种类资源调查的站位设计方案。     

基于分解机制的多目标蝙蝠算法

在分析蝙蝠算法性能基础上,将蝙蝠算法融入分解机制,提出了一种基于分解机制的多目标蝙蝠算法。为了进一步提高算法的多样性,将差分进化策略引入算法中。对14个具有复杂Pareto前沿的多目标优化问题(LZ-09系列和ZDT系列)测试不同邻域规模对算法性能的影响,结果表明新算法的邻域规模为20时性能最优;将其与MOEA/D-DE和NSGA-II算法进行对比分析,结果显示该算法的分布性、收敛性和多样性均优于另外两种算法。为了验证其求解含有约束问题的性能,将其应用于滑动轴承多目标优化设计问题中,获得的Pareto前沿分布均匀,表明算法具有工程实用性,是求解复杂高维多目标问题的有效方法。     

旋转式钵苗栽植机构多目标参数优化与试验

为了方便快捷地得到旋转式钵苗栽植机构的最佳参数,提出了旋转式钵苗栽植机构作业时实现理想栽植应满足的条件,包括栽植嘴运动学特性(轨迹、姿态、速度)、挖出穴口的几何特性、栽植机构避免干涉和非圆齿轮的保凸性要求。根据这些要求,以偏心-椭圆齿轮行星轮系栽植机构为研究对象,建立其11个子目标函数,并应用模糊理论,将多目标优化问题转化为单目标优化问题进行求解,得到满足理想栽植要求的参数,然后进行结构设计、虚拟仿真、试验台加工和模拟田间栽植试验,验证了该栽植机构的高立苗率和优化模型的正确性。该优化模型的建立和求解克服了栽植机构传统参数设计时采用仿真软件进行试凑的缺点,提高了优化效率。     

Resource allocation tradeoffs in Manila’s peri-urban vegetable production systems: An application of multiple objective programming

This paper designed and developed a multi-objective programming (MOP) model to illustrate the dynamic relationship among technologies, productive activities, constraints and farmers’ objectives in the peri-urban vegetable production system and use the model as an economic tool in analysing probable consequences of a given action or innovation on the farm. The best compromise solution was generated using four analytical steps, as follows: single-objective optimization (to determine the ideal and anti-ideal values of the objective functions); constrained optimization (to generate the set of Pareto non-dominated solutions); cluster analysis (to trim down efficient set into smaller homogeneous groups); and compromise programming (to determine where the best compromise solution lies).     

基于MOSA算法的高压共轨喷油器多目标仿真优化设计

建立了高压共轨喷油器的Matlab/Simulink仿真模型,采用多目标模拟退火MOSA优化算法作为寻优策略,以针阀开启延迟和关闭延迟为目标函数进行高压共轨喷油器的关键结构参数多目标仿真优化设计。实验结果表明:优化后的高压共轨喷油器针阀开启延迟时间降低16.7%,达到0.25 ms;关闭延迟时间降低15.2%,达到0.78 ms。     

Combining deliberative and computer-based methods for multi-objective land-use planning

This paper reports the outcomes of a deliberative workshop comparing land-use plans proposed by land-manager or domain experts with those derived using a computer-based decision support system (DSS). The DSS integrates four main components, a geographic information system, land-use systems simulation models, impact assessments and land-use planning tools. The land-use planning tools draw on the other components to generate and evaluate alternative patterns of land use and management. Since the land-use planning tools are based on multi-objective genetic algorithms (mGAs) it is possible to generate a range of alternative plans that define the structure of the trade-off between the objectives. The workshop tasked the delegates with specifying land-use plans that achieved the best compromise between two objectives known to be non-commensurable and conflicting. The nature of the best compromise was dependent on their individual perspectives. The delegates proposed allocations both as individuals and in researcher-facilitated sub-groups. The mGA allocations were then compared with those derived by delegates and were found to be broadly similar in performance. Differences in the range of allocations considered feasible were explained by the hard and soft constraints on allocations agreed between the delegates and articulated within the workshop process. The hypothesis that part of the difference in performance between the mGA and delegate allocations was due to the delegates blocking together fields with the same land use for convenience of management was proved. The analysis of the group allocations revealed that the decision-making process had failed to improve on the individual allocations. From these results it was concluded that there was a potential role for mGA based land-use planning tools in researching into, and deliberating on, the possible impacts of policy or other factors affecting land-use systems. It was further concluded that the tools should not be used in isolation since there was the need for stake-holder inputs to adequately define the range of feasible and practical land-use plans.     

辽宁省东部山区5种森林植被类型水文生态效益综合评判

利用层次分析与Fuzzy数学相结合方法,对辽宁省东部山区5种森林植被类型水源涵养能力进行了多目标综合评判。首先利用层次分析法确定了评价因子林冠截留、林冠蒸散、枯落物蓄水、土壤容重、非毛管孔隙度、初渗速率、稳渗速率、土壤总蓄水和土壤有效水的权重集为C=(0.0245,0.0051,0.1993,0.0109,0.0762,0.0565,0.2827,0.0573,0.2866),然后利用最大模糊熵原则确定了评价因子的隶属函数,最后利用Fuzzy综合评判得出油松、落叶松、红松、柞木林、杂木林评价结果模糊子集为B=(0.4686,0.3784,0.4145,0.6128,0.4808),结果表明阔叶林水源涵养效益高于针叶林,其中柞木林效益最佳。     

Differential evolution algorithm for solving multi-objective crop planning model

This study presents four strategies of a novel evolutionary algorithm, multi-objective differential evolution algorithm (MDEA). The four strategies namely, MDEA1, MDEA2, MDEA3 and MDEA4 are adapted to solve the multi-objective crop planning model with multiple constraints in a farmland in the Vaalharts irrigation scheme (VIS) in South Africa. The three objectives of the model are to minimize the total irrigation water (m²) and to maximize both the total net income in South African Rand (ZAR) from farming and the total agricultural output in tons. The total area of the farm is 771,000 m² and supplied with 704,694 m² of irrigation water annually. Numerical results produce non-dominated solutions which converge to Pareto optimal fronts. MDEA1 and MDEA2 strategies with binomial crossover method are better for solving the crop planning problem presented than MDEA3 and MDEA4 strategies with exponential crossover method. MDEA1 found a solution with the highest total net income of ZAR 1,304,600 with the corresponding total agricultural output, total irrigation water and total planting areas of 316.26 tons, 702,000 m³ and 725,000 m², respectively. The planting areas for the crops in the solution are 73,463 m² for maize, 551,660 m² for groundnut, 50,000 m² for Lucerne and 50,000 m² for Peacan nut. It can be concluded that MDEA is a good algorithm for solving crop planning problem especially in water deficient areas like South Africa.     

基于试验设计的汽油机进气系统参数多目标优化

以集成优化方法为基础,建立汽油机进气系统优化流程,结合发动机循环模拟工具Boost进行计算,并通过试验对模型进行了验证.以标定转速下的进气系统几何参数(进气管长度、直径、谐振腔容积)、点火提前角与循环喷油量为设计变量,应用DOE试验方法,对发动机性能进行多目标优化计算,归纳了进气系统关键参数对整机性能的影响及各变量参数对发动机性能的影响规律,总结了进气管长度对发动机速度特性的影响,为进气系统优化设计提供了一种高效快捷的方法.     

基于灰色关联和G（1,1）的多目标农业技术创新主体投资结构优化研究

针对多目标农业技术创新主体投资收益低于预期收益问题,提出了多目标二级灰色优化方法。在满足较低水平最优目标和灰色区间预测值内,以主体投资与较高水平目标的灰色关联度为权重,从而把多目标灰色优化转化为单目标线性规划。并给出了主体投资的非劣策略。最后对1990～2006年时间序列进行实证分析和改进区间预测,得到更加有价值的区间,并发现农户投资行为对创新效益的贡献率最大,同时得到了满足农户最优目标条件下的非劣行为策略,验证了该方法的有效性。