Solving 2D-wave problems by the iterative differential quadrature method

In this paper, the numerical stability of an iterative method based on differential quadrature (DQ) rules when applied to solve a two-dimensional (2D) wave problem is discussed. The physical model of a vibrating membrane, with different initial conditions, is considered. The stability analysis is performed by the matrix method generalized for a 2D space-time domain. This method was presented few years ago by the same author as an analytical support to check the stability of the iterative differential quadrature method in 1D space-time domains. The stability analysis confirms here the conditionally stable nature of the method. The accuracy of the solution is discussed too.     

Solution of initial value problems by the differential quadrature method with Hermite bases

The differential quadrature method (DQM) is used to solve the first-order initial value problem. The initial condition is given at the beginning of the interval. The derivative of a space-independent variable at a sampling grid point within the interval can be defined as a weighted linear sum of the given initial conditions and the function values at the sampling grid points within the defined interval. Hermite polynomials have advantages compared with Lagrange and Chebyshev polynomials, and so, unlike other work, they are chosen as weight functions in the DQM. The proposed method is applied to a numerical example and it is shown that the accuracy of the quadrature solution obtained using the proposed sampling grid points is better than solutions obtained with the commonly used Chebyshev–Gauss–Lobatto sampling grid points.     

谱元法在求解刚架结构动力学问题中的应用

把谱元法应用于刚架结构的动力学响应计算和分析中.建立了杆和梁的谱单元动力学刚度阵,针对刚架结构组装了整体动力学刚度阵,建立了整体结构的运动方程,计算了结构的固有频率和时域响应,并与采用有限元方法得到的结果进行了对比.从结果中可以看出谱元法在数值模拟中的独特优势.     

用微分求积法分析输液管道的非线性动力学行为

将微分求积法(Differential Quadrature Method,简称DQM)应用于输液管道的非线性动力学分析,采用此法研究了受非线性约束输液管道的分岔现象和混沌运动问题.从悬臂输液管道模型出发,利用微分求积法形成管道的动力学方程.以分岔图、相平面图、时间历程图和Poincare映射等分析手段考察了系统参数(管内流速)变化对管道振动形态的影响.结果表明,在所研究的系统中存在出现倍周期分岔现象和混沌运动的参数区域,这与前人的研究成果具有一致性.这为一类结构的非线性动力响应问题提供了一种新的研究思路.     

A new method for solving boundary value problems for partial differential equations

This paper proposes a symmetry–iteration hybrid algorithm for solving boundary value problems for partial differential equations. First, the multi-parameter symmetry is used to reduce the problem studied to a simpler initial value problem for ordinary differential equations. Then the variational iteration method is employed to obtain its solution. The results reveal that the proposed method is very effective and can be applied for other nonlinear problems.     

A new algorithm based on differential transform method for solving multi-point boundary value problems

In this work, an efficient algorithm based on the differential transform method is applied to solve the multi-point boundary value problems. The solution obtained by using the proposed method takes the form of a convergent series with easily computable components. Several numerical examples, both linear and nonlinear, are given to testify the validity and applicability of the proposed method. Comparisons are made between the present method and the other existing methods.     

An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games

In this paper, a new iterative adaptive dynamic programming (ADP) method is proposed to solve a class of continuous-time nonlinear two-person zero-sum differential games. The idea is to use the ADP technique to obtain the optimal control pair iteratively which makes the performance index function reach the saddle point of the zero-sum differential games. If the saddle point does not exist, the mixed optimal control pair is obtained to make the performance index function reach the mixed optimum. Stability analysis of the nonlinear systems is presented and the convergence property of the performance index function is also proved. Two simulation examples are given to illustrate the performance of the proposed method.     

On solving periodic re-optimization dynamic vehicle routing problems

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. One of its important variants is the Dynamic Vehicle Routing Problem (DVRP) in which not all customers are known in advance, but are revealed as the system progresses. DVRP is a Dynamic Optimization Problem (DOP) that has become a challenging research topic in the past two decades. In DOPs, at least one part of the problem changes as time passes. For DVRP, customers change as a system progresses. Consequently, DVRP applications are seen to operate on a dynamic basis in various real-life systems. To date, a time-based evaluation approach has been used to evaluate periodic re-optimized DVRP systems, which are evaluated by solving while using a specific time budget. In this paper, we solve DVRP while using an enhanced Genetic Algorithm (GA) that tries to increase both diversity and the capability to escape from local optima. Also, we propose a fair weighted fitness evaluation approach as an alternative for the biased time-based approach, regardless of the specifications and power of the running system. The proposed enhanced GA outperformed the previously published algorithms based on both the time-based and weighted fitness evaluation approaches.     

A T-cell algorithm for solving dynamic optimization problems

In this paper, a metaheuristic inspired on the T-Cell model of the immune system (i.e., an artificial immune system) is introduced. The proposed approach (called DTC, for Dynamic T-Cell) is used to solve dynamic optimization problems, and is validated using test problems taken from the specialized literature on dynamic optimization. Results are compared with respect to artificial immune approaches representative of the state-of-the-art in the area. Some statistical analyses are also performed, in order to determine the sensitivity of the proposed approach to its parameters.     

Using dynamic programming for solving variational problems invision

Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed     

Numerical simulation of two-dimensional sine-Gordon solitons by differential quadrature method

During the past few decades, the idea of using differential quadrature methods for numerical solutions of partial differential equations (PDEs) has received much attention throughout the scientific community. In this article, we proposed a numerical technique based on polynomial differential quadrature method (PDQM) to find the numerical solutions of two-dimensional sine-Gordon equation with Neumann boundary conditions. The PDQM reduced the problem into a system of second-order linear differential equations. Then, the obtained system is changed into a system of ordinary differential equations and lastly, RK4 method is used to solve the obtained system. Numerical results are obtained for various cases involving line and ring solitons. The numerical results are found to be in good agreement with the exact solutions and the numerical solutions that exist in literature. It is shown that the technique is easy to apply for multidimensional problems.     

改进差异演化算法求解约束优化问题

在现实生活中许多实际问题都可以转化为约束优化问题,并且实际问题通常都很复杂,其函数形态各具特色,传统基于梯度信息的各种求解策略对于具有不可微、多峰及非凸的非线性函数约束优化问题很难凑效。而最近兴起的智能类算法却对这类问题的求解效果突出,在借鉴国外的差异演化算法研究成果基础上,运用改进差异演化算法来求解约束优化问题。最后通过实例进行仿真实验,结果表明改进差异演化算法在求解约束优化问题时具有一定的优越性。     

Solution of magnetohydrodynamic flow in a rectangular duct by differential quadrature method

The polynomial based differential quadrature and the Fourier expansion based differential quadrature method are applied to solve magnetohydrodynamic (MHD) flow equations in a rectangular duct in the presence of a transverse external oblique magnetic field. Numerical solution for velocity and induced magnetic field is obtained for the steady-state, fully developed, incompressible flow of a conducting fluid inside of the duct. Equal and unequal grid point discretizations are both used in the domain and it is found that the polynomial based differential quadrature method with a reasonable number of unequally spaced grid points gives accurate numerical solution of the MHD flow problem. Some graphs are presented showing the behaviours of the velocity and the induced magnetic field for several values of Hartmann number, number of grid points and the direction of the applied magnetic field.     

云环境下求解大规模优化问题的协同差分进化算法

差分进化是一种求解连续优化问题的高效算法。然而差分进化算法求解大规模优化问题时,随着问题维数的增加,算法的性能下降,且搜索时间呈指数上升。针对此问题,本文提出了一种新的基于Spark的合作协同差分进化算法（SparkDECC）。SparkDECC采用分治策略,首先通过随机分组方法将高维优化问题分解成多个低维子问题,然后利用Spark的弹性分布式数据模型,对每个子问题并行求解,最后利用协同机制得到高维问题的完整解。通过在13个高维测试函数上进行的对比实验和分析,实验结果表明算法加速明显且可扩展性好,验证了SparkDECC的有效性和适用性。     

Easily solving dynamic programming problems in Haskell by memoization of hylomorphisms

Dynamic programming is a well-known algorithmic technique that solves problems by a combination of dividing a problem into subproblems and using memoization to avoid an exponential growth of the costs. We show how to implement dynamic programming in Haskell using a variation of hylomorphisms that includes memoization. Our implementation uses polymorphism so the same function can return the best score or the solution to the problem based on the type of the returned value.     

Differential dynamic programming methods for solving bang-bang control problems

Differential dynamic programming is a technique, based on dynamic programming rather than the calculus of variations, for determining the optimal control function of a nonlinear system. Unlike conventional dynamic programming where the optimal cost function is considered globally, differential dynamic programming applies the principle of optimality in the neighborhood of a nominal, possibly nonoptimal, trajectory. This allows the coefficients of a linear or quadratic expansion of the cost function to be computed in reverse time along the trajectory: these coefficients may then be used to yield a new improved trajectory (i.e., the algorithms are of the "successive sweep" type). A class of nonlinear control problems, linear in the control variables, is studied using differential dynamic programming. It is shown that for the free-end-point problem, the first partial derivatives of the optimal cost function are continuous throughout the state space, and the second partial derivatives experience jumps at switch points of the control function. A control problem that has an aualytic solution is used to illustrate these points. The fixed-end-point problem is converted into an equivalent free-end-point problem by adjoining the end-point constraints to the cost functional using Lagrange multipliers: a useful interpretation for Pontryagin's adjoint variables for this type of problem emerges from this treatment. The above results are used to devise new second- and first-order algorithms for determining the optimal bang-bang control by successively improving a nominal guessed control function. The usefulness of the proposed algorithms is illustrated by the computation of a number of control problem examples.     

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.