舰载武器系统研制方案多目标生成与评价方法

舰载武器系统研制方案的生成、评价与优选问题是武器装备研制论证中的重要内容．在分析目前舰载武器系统研制方案论证方法的基础上，构建了舰载武器系统多目标优化模型，提出了舰载武器系统研制方案的多目标生成方法，并建立了不完全偏好信息的舰载武器系统研制方案的多目标评价与优选模型．实例分析说明，该模型与方法是有效的，可为武器装备研制论证问题提供辅助决策方法和智力支持．     

基于遗传模拟退火算法的空袭兵力分配及优化

对遗传模拟退火算法中的交叉、变异操作进行了改进，并实施了最优保留策略，形成了改进遗传模拟退火算法．以突击效果最大化和兵力损失最小化为目标函数，以空袭兵力总量的限制、空袭兵器挂载类型的限制等为约束条件，建立了空袭兵力分配及优化模型．在考虑兵力分配模型特点的基础上，利用改进遗传模拟退火算法求解．通过与多目标数学规划和标准遗传算法优化进行的比较表明，该方法能够有效地解决带约束的多目标优化问题．     

多目标遗传算法在电梯主动导轮系统集成优化设计中的应用

介绍了一种基于多目标遗传算法的电梯主动导轮系统集成结构和控制器优化设计方法。在本方法中．电梯主动导轮结构参数和主动控制器参数同时作为优化设计变量，电梯系统性能和控制代价作为优化设计目标。这是一个复杂的多目标优化问题。由于控制器具有特定的结构，多目标遗传算法直接被用来求解该多目标集成优化问题。这样不仅可以避免权函数的选择，而且一次运行就可以求出所有的Pareto最优解。最后用一个实验室比例电梯模型作为例子来说明该设计过程。     

模糊可靠约束下的普通蜗杆传动多目标设计

 在普通优化设计的基础上，提出一种新的蜗杆传动的设计方法． 应用模糊数学理论和可靠设计方法，建立了模糊可靠约束下普通圆柱蜗杆传动的多目标优化的数学模型，并对实例进行了计算分析．结果表明，模糊可靠约束下蜗杆传动的多目标优化设计是可行和实用的．     

广义二阶不变凸向量变分类不等式

研究了广义二阶不变凸向量变分类不等式问题解的存在性，并讨论其与多目标优化问题解之间的关系。引入了两种广义二阶不变凸函数和一种广义二阶单调函数，并给出具体实例说明了它们的存在性。在广义二阶不变凸性假设下，利用分析的方法给出了向量变分类不等式与多目标优化问题解之间的关系。利用KKM定理，在广义二阶单调性假设下得到了向量变分类不等式问题解的存在性定理。研究表明在广义二阶单调性假设下，向量变分类不等式存在解，并且在适当的广义二阶凸性条件下，其解与多目标优化问题解之间相互等价。     

基于个体判断优先序的多目标群决策方法研究

针对多目标群决策问题，深入分析了群意见的形成方式．对基于方案优先序的多目标群决策问题给出了若干求解群排序的方法．对其求解原理和集结结果进行了对比，并分析了各方法的实质及其等价关系；澄清了一些模糊认识，指出了某些方法存在的问题，对此类多目标群决策问题的求解提供了依据．     

船舶动力机械混合隔振系统的优化设计

综合分析了混合隔振的原理，揭示了混合隔振的实质就是被动隔振系统和主动隔振系统的多目标联合优化问题．针对船舶中常用的双层隔振系统，进行了混合隔振优化设计和数值仿真．仿真结果表明，混合隔振技术在船舶动力机械隔振方面有着广阔的应用前景．     

基于差异演化算法和多属性决策的机电系统可靠性多目标优化设计

针对机电系统可靠性设计问题,以可靠性和费用(或体积等)最优为目标建立可靠性设计的多目标优化模型．提出了自适应多目标差异演化算法,该算法提出了自适应缩放因子和混沌交叉率,采用改进的快速排序方法构造Pareto最优解,采用NSGA-II的拥挤操作对档案文件进行消减．采用自适应多目标差异演化算法获得多目标问题的Pareto最优解,利用TOPSIS方法对Pareto最优解进行多属性决策．实际工程结果表明：自适应多目标差异演化算法调节参数更少,且求得的Pareto最优解分布均匀;采用基于TOPSIS的多属性决策方法得到的结果合理可行．     

一般多阶段有补偿问题的间接单阶段化及其应用

对陈志平在1992年博士学位论文中所讨论的一般多阶段有补偿问题，通过引进松弛函数，给出了一个恰当、合理的，其目标函数与约束函数均不太复杂的单阶段化问题．并论证丁二者之间的等价性，从而克服了巳有单阶段化方法的缺点．在讨论上述结果的应用中．特别是利用已有半无限规划算法导出了求解一般非线性多阶段有补偿问题的积分罚函数法．为求解该类问题提供了一种新的途径．     

多目标优化软件开发及其应用

针对基于传统数学规划原理的多目标优化方法在实际工程优化问题中的脆弱性及现有一些多目标优化方法在应用时具有的缺陷性,提出了一种将改进粒子群算法与灰色决策相结合的多目标优化方法.联合MATLAB和VB,开发了一款基于该方法的便于工程应用的多目标优化软件.将该软件应用于掘进机铲板参数多目标优化,取得了良好的优化效果,验证了该方法的可行性,为工程实际中处理多目标优化问题提供了便利与借鉴.     

Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.     

Using a derivative-free optimization method for multiple solutions of inverse transport problems

Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a mesh adaptive direct search (MADS) algorithm is incorporated into the local phase. Numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.     

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task’s completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task’s completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.     

面向批量订单包装的物流箱规格优化问题研究

目的 针对面向仓储物流环境下多型号多批量产品的订单包装问题,提出一种预制物流箱规格优化模型及算法。方法 对产品订单建立订单分包规则,确定分包方案,以订单包装材料总成本最小为优化目标建立物流箱规格优化模型。针对该模型提出一种改进模拟退火算法,通过贪婪策略求解最优分包方案,降低模型计算复杂度,设计一种新型解更新算子,以提高算法寻优能力,设计一种自适应步长策略,以平衡算法前期全局搜索与后期局部搜索的能力。结果 通过实例证明,文中提出的算法相较于其他算法,具有更强的求解能力,与实例企业仓储包装现状相比,同批订单降低了17%的包装材料成本。结论 该方法可用于解决产品种类多、尺寸差异大、动态更新等应用场景下的系列运输包装纸箱规格优化问题,为企业物流运输管理提供了一种有效的包装优化思路和解决方法。     

A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.     

Multiple colony ant algorithm for job-shop scheduling problem

Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems.     

A Bound Heuristic Technique for Solving DRAMA Spares Optimizations

1IntroductionTodaythePublicrequiresthatallcomplexellgilleering,suchasatomicPOwerplants,airCrafl,automobilesandcomputer,etc.,behighlyreliable.Generally,tilesystemsarerepairableinventorysystemsthatarecomposedofitemswhicharerepairedalldretUrnedtouseratherthandiscarded.TherepairableinventoryProblemistypicallyconcernedwilhtheoptimalstockingofpartsatbases(orforwardlocations)andacentereddelx)t1'llcilitywhichrepairedunitsreturnedfromthehaseswhileprovidingsomePredeterminedlevelofservice.Themathemati…     

A study on evolutionary multi-objective optimization for flow geometry design

In this paper, we investigate three recently proposed multi-objective optimization algorithms with respect to their application to a design-optimization task in fluid dynamics. The usual approach to render optimization problems is to accumulate multiple objectives into one objective by a linear combination and optimize the resulting single-objective problem. This has severe drawbacks such that full information about design alternatives will not become visible. The multi-objective optimization algorithms NSGA-II, SPEA2 and Femo are successfully applied to a demanding shape optimizing problem in fluid dynamics. The algorithm performance will be compared on the basis of the results obtained.     

基于改进粒子群算法的月地转移轨道优化

月地转移轨道优化是月球返回任务的技术难题之一,其搜索空间大、约束条件多。该文通过罚函数法将多约束优化问题转化为无约束优化问题,提出了一种改进粒子群算法,利用适应度函数来更新惯性权重,对粒子的速度加以约束,还对粒子的位置参数引入随机反馈控制,分析了算法的收敛性。在月地返回窗口内获得了逃逸速度增量最小的月地转移轨道优化结果,并利用目标函数的等高线图分析,对优化结果进行了验证。     

A two-stage approach for multi-objective decision making with applications to system reliability optimization

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.