随机线性路径函数在稳相法重建中的应用

为了提高稳相分级模拟退火算法在三维重建上的精度,引入随机线性路径函数,与两点相关函数同时约束重建过程,借助稳相模拟退火思想,最终重建出三维图像。针对大量岩心二维图像进行实验分析,结果表明:相较于单约束条件,改进的重建算法能够显著提高重建精度,保存更多原始图像的形态学特性。     

雷达干扰资源优化分配模型和算法研究北大核心CSCD

针对敌方多目标雷达,如何合理有效的分配干扰资源,取得最佳干扰效益的问题,建立干扰资源优化分配模型。考虑到实际作战环境下的约束条件和干扰需求,对于约束条件增多时,传统算法求解速度慢。因此,提出一种将模拟退火算法应用到遗传算法中,以提高遗传算法局部搜索性能,增强遗传算法进化能力的遗传模拟退火算法。在每次进化产生下一代种群个体需经过模拟退火算法改进,并在每次迭代结束之前进行降温操作保证遗传算法和模拟退火算法具有相同的收敛方向和速度。仿真结果表明,与模拟退火算法比较,该方法具有较好的搜索最优解速度和可靠性。上述方法提供的分配方案对提高雷达干扰智能决策系统有一定的作用。     

基于模拟退火智能算法的自抗扰控制器参数整定及仿真

针对自抗扰控制器（ADRC）参数多以及由于无确定参数整定算法导致的难以计算最优参数的问题,提出了结合已知参数整定规则的智能模拟退火算法（SA）．该方法改进了原始模拟退火算法的搜索规则,提高了参数搜索的范围和效率．最后,通过仿真实例验证了这种改进的智能模拟退火算法的有效性．     

约束优化问题的混合遗传算法研究

如何处理约束条件与增强局部搜索能力是遗传算法用于非线性约束优化问题的线性约束优化问题的不足,提出了一种基于模拟退火算法与外点法的混合遗传算法,对于不满足约束条件的解用外点罚函数法来修正,同时把退火选择算子作为一个与选择、交叉和变异平行的算子,嵌入到实数编码的遗传算法中,来增强其的局部搜索能力.算法兼顾了遗传算法、模拟退火算法和外点法三者的长处,既有较快的收敛速度,又能以较大的概率求得非线性约束优化问题的全局最优解.最后以两个测试函数为算例对算法进行测试,验证了该算法搜索能力强、稳健性好,能获得更好的优化结果.实验结果表明引入外点法处理约束条件是可行的.     

基于模拟退火遗传算法的多项目调度问题研究

针对多资源约束条件下的多项目调度问题,提出了一种模拟退火遗传算法的求解方法.该方法首先分别对普通的遗传算法和模拟退火算法进行改进,然后在遗传算法中插入模拟退火操作,通过模拟退火操作来克服遗传算法容易陷入局部最优解的缺陷,同时该方法也继承了遗传算法收敛速度快的特点.最后的实例计算结果表明该算法能克服模拟退火算法和遗传算法的缺点,获得比其它算法更优的解,与其它启发式算法及智能算法相比具有更高的求解效率.     

模拟退火算法在频率指配中的应用优化

已知发射机坐标和可用频率,考虑同、邻频约束和入口覆盖,建立了频率指配的数学模型.即在满足同、邻频约束条件下,寻求一组频率使得每台发射机尽量指配可用频率中的最低频率(无可用频率的将不被指配),并使得入口覆盖率最高.以局部搜索算法为参照,将模拟退火算法应用到频率指配问题中,结果表明模拟退火算法的指配结果质量明显优于局部搜索算法.并针对模拟退火算法的耗时性使用OpenMP指令优化约束检侧代码,在多核计算机上运行取得了很好的加速效果.     

多约束三维装箱问题的混合遗传算法

三维装箱问题提出至今已有很多研究成果,各种启发式算法配合遗传算法、蚁群算法和模拟退火算法的设计层出不穷。而针对于三维装箱问题的各种约束,虽然各自有相应的处理方法,但却没有一种方法可以整合各种约束条件,这是因为启发式算法往往容易满足部分约束却很难满足所有约束的特点。在前人研究的基础上,针对各种遗传算法的约束条件,设计可以相互组合的解决各种约束条件的算法,通过对这些算法规则组合,可以解决各种约束条件下的三维装箱问题。     

干扰目标优化分配模型和算法的研究

电子干扰成功压制威胁目标对突防作战起着关键性的作用,针对理想环境下优化模型的不足,考虑到实战环境下的约束条件和干扰需求,构建了干扰目标的优化分配模型;对于参变量和约束条件增多时,传统算法求解速度慢,甚至无法求得最优解,为此,引入遗传算法,改进编码、染色体和遗传算子的设计,通过具体算例建立模型和求解,并与模拟退火算法进行比较分析,结果表明改进遗传算法搜索最优解的速度和可靠性都优于模拟退火算法。最后给出了干扰目标分配的最优方案,为实现干扰的最佳压制效能提供了科学决策。     

多约束三维装箱问题的混合遗传算法

三维装箱问题提出至今已有很多研究成果,各种启发式算法配合遗传算法、蚁群算法和模拟退火算法的设计层出不穷。而针对于三维装箱问题的各种约束,虽然各自有相应的处理方法,但却没有一种方法可以整合各种约束条件,这是因为启发式算法往往容易满足部分约束却很难满足所有约束的特点。在前人研究的基础上,针对各种遗传算法的约束条件,设计可以相互组合的解决各种约束条件的算法,通过对这些算法规则组合,可以解决各种约束条件下的三维装箱问题。     

基于动态规划和模拟退火算法的排课系统

在分析中小学的排课问题并研究相关算法的基础上,提出了把动态规划算法和模拟退火算法相结合的一种新的排课算法.动态规划算法求出满足约束条件的一个解,作为模拟退火算法的初始解,用模拟退火算法对初始解优化,得到一个优化后的课表.     

基于遗传算法的数值优化约束问题的研究

针对数值优化约束中出现的大规模、多峰多态函数,含离散变量等情况下的全局优化问题,采用常规的优化方法,收敛速度较慢,求得全局极值的概率较低.提出用遗传算法的数值优化约束问题解决,通过数值仿真实验结果表明,该算法性能优于现有其它算法,它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是高效稳健的智能算法,具有很高的全局寻优能力和很快的收敛速度,对求解复杂多峰多态函数的优化约束问题具有可行性和有效性.     

改进狼群算法用于多约束稀布线阵综合

结合量子理论提出了一种改进狼群算法,并将其用于优化多约束稀布直线阵列综合问题。新算法通过量子位特殊编码方式、停滞检测与选择性变异极大地提高了全局优化能力。给出了改进狼群算法流程,并在给定阵列孔径和阵元数的条件下,实现了任意最小阵元间距约束下,抑制天线峰值旁瓣电平（PSLL）的稀布线阵综合仿真。通过解空间变换,有效避免了算法进行阵列综合时,狼群位置更新过程中出现不可行解问题,减少了判断步骤,提高了优化效率。通过典型实例的仿真对比,证实了该方法的有效性和稳健性,而且能获得比现有方法更低的PSLL和更高的优化效率。     

Integral Approximation of Rays and Verification of Feasibility

An algorithm is presented that produces an integer vector nearly parallel to a given vector. The algorithm can be used to discover exact rational solutions of homogeneous or inhomogeneous linear systems of equations, given a sufficiently accurate approximate solution.As an application, we show how to verify rigorously the feasibility of degenerate vertices of a linear program with integer coefficients, and how to recognize rigorously certain redundant linear constraints in a given system of linear equations and inequalities. This is a first step towards the handling of degeneracies and redundandies within rigorous global optimization codes.     

超导MRI主磁体优化设计的模拟退火算法

在进行MRI(magneticresonanceimaging)超导主磁体的设计时常采用优化设计的方法,将各设计参数看作连续变量处理,但实际上很多参数是离散变量,为了更符合工程实际,将超导MRI主磁体的设计作为一个含有离散变量的全局优化问题。建立了适用于多种超导MRI主磁体结构的数学模型,包括设计变量、目标函数、约束条件等,选用了适用于MRI超导主磁体优化设计的含有离散变量的模拟退火算法进行设计。算例结果表明,本文选取的数学模型和优化算法是有效的,能够达到超导MRI主磁体设计的要求。     

基于混合粒子群算法的烧结配料优化

在引入惩罚函数和对目标函数进行适当修改的前提下,充分利用粒子群优化算法的全局搜索能力和约束条件下共轭梯度法的局部搜索能力,设计了烧结配料优化算法．利用惩罚函数方法将约束条件优化问题转化为无约束条件优化问题,然后利用粒子群优化算法进行寻优．当群体最优信息陷入停滞时将目标函数进行适当变化,继续利用共轭梯度法进行寻优．计算结果表明,采用该方法能够在提高混合料中的有用成分、降低有害成分的前提下,更多地降低生产成本．     

Community detection in complex networks: Multi-objective discrete backtracking search optimization algorithm with decomposition

Community detection is believed to be a very important tool for understanding both the structure and function of complex networks, and has been intensively investigated in recent years. Community detection can be considered as a multi-objective optimization problem and the nature-inspired optimization techniques have shown promising results in dealing with this problem. In this study, we present a novel multi-objective discrete backtracking search optimization algorithm with decomposition for community detection in complex networks. First, we present a discrete variant of the backtracking search optimization algorithm (DBSA) where the updating rules of individuals are redesigned based on the network topology. Then, a novel multi-objective discrete method (MODBSA/D) based on the proposed discrete variant DBSA is first proposed to minimize two objective functions in terms of Negative Ratio Association (NRA) and Ratio Cut (RC) of community detection problems. Finally, the proposed algorithm is tested on some real-world networks to evaluate its performance. The results clearly show that MODBSA/D has effective and promising performance for dealing with community detection in complex networks.     

Multi-layer optimization in service-oriented sensor grid

Sensor devices such as video cameras, infrared sensors and microphones are being widely exploited in grid application. The paper deals with multi-layer optimization in service oriented sensor grid to optimize utility function of sensor grid, subject to resource constraints at resource layer, service composition constraints at service layer and user preferences constraints at application layer respectively. The multi-layer optimization problem can be decomposed into three subproblems: sensor grid resource allocation problem, service composing problem, and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of sensor grid resources and service demand. The proposed algorithm decomposes global sensor grid optimization problem into a sequence of three sub-problems at three layers via an iterative algorithm. The simulations are conducted to validate the efficiency of the multi-layer optimization algorithm. The experiments compare the performance of the multi-layer global optimization approach with application layer local optimization and resource layer local optimization approach respectively.     

结合分支定界法和线性规划的摄像机位姿估计

介绍了一种新的利用对应点估计摄像机位姿的算法。通常情况下,摄像机位姿估计可以转化为一个最优化问题,现有算法将问题转换成一个序列二阶锥规划问题,通过对旋转矩阵所在空间进行分支定界搜索来求取全局最优解。对现有算法进行改进,通过将二阶锥约束松弛为线性约束,提出了一种结合分支定界法和线性规划方法的全局优化算法。该算法不仅能够求得全局最优解,而且算法速度较现有算法提高了一倍以上。最后通过模拟数据和真实数据对该算法进行了验证,结果表明了该算法的准确性和高效性。     

Hexahedral mesh smoothing via local element regularization and global mesh optimization

The quality of finite element meshes is one of the key factors that affect the accuracy and reliability of finite element analysis results. In order to improve the quality of hexahedral meshes, we present a novel hexahedral mesh smoothing algorithm which combines a local regularization for each hexahedral mesh, using dual element based geometric transformation, with a global optimization operator for all hexahedral meshes. The global optimization operator is composed of three main terms, including the volumetric Laplacian operator of hexahedral meshes and the geometric constraints of surface meshes which keep the volumetric details and the surface details, and another is the transformed node displacements condition which maintains the regularity of all elements. The global optimization operator is formulated as a quadratic optimization problem, which is easily solved by solving a sparse linear system. Several experimental results are presented to demonstrate that our method obtains higher quality results than other state-of-the-art approaches.     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.