一种求解约束优化问题的演化规划算法

提出了一种新的求解约束优化问题的演化算法--基于混合策略求解约束优化问题的演化规划算法（CMSEP）.借鉴了Mezura-Montes的算法中直接比较的约束处理方法,为求解位于边界附近的全局最优解采用多样性保护机制,允许一定比例最好不可行解进入下一代种群,混合策略变异机制用于指导算法快速搜索过程.标准测试函数的实验结果验证了算法的通用性和有效性.     

约束优化进化算法综述

约束优化进化算法主要研究如何利用进化计算方法求解约束优化问题,是进化计算领城的一个重要研究课题.约束优化问题求解存在约束区域离散、等式约束、非线性约束等挑战,其问题的本质是如何处理可行解与不可行解的关系才能使得算法更高效.本文首先介绍了约束优化问题的定义,然后系统地分析了目前存在的约束优化方法,同时基于约束处理机制将这些方法分为罚函数法、可行性法则、随机排序法、约束处理法、多目标优化法、混合法六类,并从约束处理方法的方面对约束优化进化算法的最新研究进展进行综述.最后,指出约束优化进化算法需进一步研究的方向与关键问题.     

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。     

解约束规划问题的新型多目标粒子群优化算法

给出了一种求解约束规划问题的新解法。新方法将约束规划问题转化成两个目标优化问题,并对转化后的多目标优化问题设计了一种新型多目标粒子群优化算法(MOPSO)。数据实验表明该算法对带约束的规划问题求解是非常有效的。     

面向复杂约束优化问题的进化算法综述

约束优化是多数实际工程应用优化问题的呈现方式.进化算法由于其高效的表现,近年来被广泛应用于约束优化问题求解.但约束条件使得问题解空间离散、缩小、改变,给进化算法求解约束优化问题带来极大挑战.在此背景下,融合约束处理技术的进化算法成为研究热点.此外,随着研究的深入,近年来约束处理技术在复杂工程应用问题优化中得到了广泛发展,例如多目标、高维、等式优化等.根据复杂性的缘由,将面向复杂约束优化问题的进化优化分为面向复杂目标的进化约束优化算法和面向复杂约束场景的进化算法两种类别进行综述,其中,重点探讨了实际工程应用的复杂性对约束处理技术的挑战和目前研究的最新进展,并最后总结了未来的研究趋势与挑战.     

Pareto强度值演化算法求解约束优化问题

提出了一种求解约束函数优化问题的方法.它不使用传统的惩罚函数,也不区分可行解和不可行解.新的演化算法将约束优化问题转换成两个目标优化问题,其中一个为原问题的目标函数,另一个为违反约束条件的程度函数.利用多目标优化问题中的Pareto优于关系,定义个体Pareto强度值指标以便对个体进行排序选优,根据Pareto强度值排序和最小代数代沟模型设计出新的实数编码遗传算法.对常见测试函数的数值实验证实了新方法的有效性、通用性和稳健性,其性能优于现有的一些演化算法.特别是对于一些既有等式约束又有不等式约束的复杂非线性规划问题,该算法获得了更高精度的解.     

采用不可微精确罚函数的约束优化演化算法

针对多数已有的采用罚函数的约束优化遗传算法存在优化效果差的问题 ,提出了一种新的求解约束优化问题的演化算法 .借助不可微精确罚函数把约束问题转化为单个无约束问题来处理 .采用混合杂交和间歇变异来提高算法的搜索能力 .数值实验结果表明了新算法的优化效果远远优于已有的几种采用罚函数的遗传算法     

求解约束优化的一个自适应杂交差分演化算法

结合基于可行性规则的约束处理技术,构造了一个求解约束优化问题的自适应杂交差分演化模拟退火算法。该算法以差分演化算法为基础,用模拟退火策略来增强种群的多样性,用一个基于可行性规则的约束处理技术来处理不等式约束,且自适应化关键控制参数,避开人为控制参数的困难。在标准测试集上的实验结果表明该算法的有效性,与同类算法的比较表明了该算法的优越性。     

求解约束优化问题的多目标粒子群算法*

提出一种多目标粒子群算法处理约束优化问题(MOCPSO). 首先将约束优化问题转化为多目标问题, 然后给出一个不可行阈值来充分地利用不可行粒子的信息引导种群的飞行; 并提出一种粒子间的比较准则以比较它们的优劣; 最后, 为了增加种群的多样性, 提升种群跳出局部最优解的能力, 引入高斯白噪声扰动. 选取有代表性的标准测试函数对MOCPSO算法的性能进行仿真实验, 相比较其它算法, 结果显示MOCPSO算法是求解约束优化问题的有效算法.     

QPSO多目标优化算法解约束规划问题

引入了一种最新而实用的方法--基于目标加权的QPSO多目标优化算法的理论应用于约束规划问题,WAQPSO算法是为了更好的解决多目标优化问题而产生的,仿真实验结果表明,WAQPSO算法具有比S-PAES算法和HM算法更强的求解约束规划问题的能力.     

曲面的自适应三角网格剖分

在传统的映射法基础上 ,采用自适应三角网格加密法能有效地处理带有特征约束条件的任意曲面的三角剖分问题 .在平面三角化算法中对环边统一处理 ,并且采取了一种简单有效的曲率估算方法 ,提高了运行效率 ;并在保持外观的基础上进行了网格质量的优化     

Reconstruction from non-uniform samples: A direct,variational approach in shift-invariant spaces

We propose a new approach for signal reconstruction from non-uniform samples, without any constraint on their locations. We look for a function that minimizes a classical regularized least-squares criterion, but with the additional constraint that the solution lies in a chosen linear shift-invariant space—typically, a spline space. In comparison with a pure variational treatment involving radial basis functions, our approach is resolution dependent; an important feature for many applications. Moreover, the solution can be computed exactly by a fast non-iterative algorithm, that exploits at best the particular structure of the problem.     

A recurrent Newton algorithm and its convergence properties.

In this paper a recurrent Newton algorithm for an important class of recurrent neural networks is introduced. It is noted that a suitable constraint must be imposed on recurrent variables to ensure proper convergence behavior. The simulation results show that the proposed Newton algorithm with the suggested constraint performs uniformly better than the backpropagation algorithm and the Newton algorithm without the constraint, in terms of mean-squared errors.     

Solution of a non-convex optimization arising in PI/PID control design

As shown by Åström et al. (Automatica 34(5) (1998) 585), the problem of designing a stabilizing PI controller based on minimizing integral of error associated with step load disturbance while subjecting to constraints on maximum sensitivity and/or complementary sensitivity amounts to that of finding the maximum allowable integral gain. The latter problem is a non-convex optimization problem whose true solution cannot be obtained with a guarantee by a gradient-based search algorithm. In this paper, we present a novel and effective approach to solve such a non-convex optimization problem. Our approach is based on regarding an equality constraint set on controller gain parameters as a two-dimensional value set in the complex plane and using the notion of principal points to characterize its boundary. With this treatment, we are able to derive analytical expressions for describing the boundary of an equality constraint set in the controller gain plane. These expressions allow one to trace the boundaries of equality constraint sets using an existing path-following algorithm. Hence, by constructing the boundary of the feasible domain in the controller gain space, the maximum allowable integral gain can be obtained. In addition to having the ability to obtain global optimal solution, our approach can handle sensitivity and complementary sensitivity constraints simultaneously without using an iterative procedure.     

Geometric constraint solving with geometric transformation

This paper proposes two algorithms for solving geometric constraint systems. The first algorithm is for constrained systems without loops and has linear complexity. The second algorithm can solve constraint systems with loops. The latter algorithm is of quadratic complexity and is complete for constraint problems about simple polygons. The key to it is to combine the idea of graph based methods for geometric constraint solving and geometric transformations coming from rule-based methods.     

An Efficient Bounds Consistency Algorithm for the Global Cardinality Constraint

Previous studies have demonstrated that designing special purpose constraint propagators can significantly improve the efficiency of a constraint programming approach. In this paper we present an efficient algorithm for bounds consistency propagation of the generalized cardinality constraint (gcc). Using a variety of benchmark and random problems, we show that on some problems our bounds consistency algorithm can dramatically outperform existing state-of-the-art commercial implementations of constraint propagators for the gcc. We also present a new algorithm for domain consistency propagation of the gcc which improves on the worst-case performance of the best previous algorithm for problems that occur often in applications.     

一种基于图分解的几何约束求解方法

为了提高几何约束求解的效率和鲁棒性 ,对基于图的构造方法进行了改进 ,即加入虚约束进行扩展和过约束问题的一致性判定 ,提出了一种基于图分解的方法 ,用此方法可以处理包括完全约束、过约束和欠约束等多种情况的约束求解问题 ,另外 ,在该方法中还通过引入分解树将约束求解的范围由整体下降到局部 ,使大部分求解过程能够采用几何求解实现 ,提高了求解和后续修改的效率 ,通过实验数据测试证明 ,该方法对于大型约束求解问题可以达到实时处理的效果 ,具有较强的实用性     

A method for enforcing integrability in shape from shadingalgorithms

An approach for enforcing integrability, a particular implementation of the approach, an example of its application to extending an existing shape-from-shading algorithm, and experimental results showing the improvement that results from enforcing integrability are presented. A possibly nonintegrable estimate of surface slopes is represented by a finite set of basis functions, and integrability is enforced by calculating the orthogonal projection onto a vector subspace spanning the set of integrable slopes. The integrability projection constraint was applied to extending an iterative shape-from-shading algorithm of M.J. Brooks and B.K.P. Horn (1985). Experimental results show that the extended algorithm converges faster and with less error than the original version. Good surface reconstructions were obtained with and without known boundary conditions and for fairly complicated surfaces     

航天器自主规划问题的建模研究

针对智能航天器的在线自主规划问题,提出了一种基于活动模型的建模方法.该方法采用规划域定义语言(Planning Domain Definition Language, PDDL)对航天器的各个活动进行表示,最终建立航天器的规划领域模型.针对规划问题中资源约束和定性定量时间约束的表达问题,采用扩展基本活动模型的方法,通过对基本活动模型添加等式和不等式,最终解决了自主规划问题中的约束建模问题.最后以一类光学成像卫星为对象,建立了其自主规划模型并开发了相应的导引式求解算法.仿真结果表明,建模方法和算法是有效的.