改进的遗传蚁群混合算法在 TSP 中的应用

针对遗传算法和蚁群算法的不足,提出一种改进的遗传蚁群混合算法。该混合算法通过判定最优解的改良情况,将遗传算法和蚁群算法动态串行融合,以充分利用遗传算法的全局搜索能力和蚁群算法的正反馈机制。同时,依据信息素在正反馈过程中的重要作用,提出一种改进的带奖惩项的信息素更新机制。仿真计算结果表明,本文提出的混合算法在求解TSP方面,收敛速度和求解质量均较传统的遗传算法及蚁群算法要好。     

带不等式路径约束最优控制问题的惩罚函数法

控制变量参数化（Control variable parameterization,CVP）方法是目前求解流程工业中最优操作问题的主流数值方法,但如果问题中包含路径约束,特别是不等式路径约束时,CVP方法则需要考虑专门的处理手段.为了克服该缺点,本文提出一种基于L1精确惩罚函数的方法,能够有效处理关于控制变量、状态变量、甚至控制变量/状态变量复杂耦合形式下的不等式路径约束.此外,为了能使用基于梯度的成熟优化算法,本文还引进了最新出现的光滑化技巧对非光滑的惩罚项进行磨光.最终得到了能高效处理不等式路径约束的改进型CVP架构,并给出相应数值算法.经典的带不等式路径约束最优控制问题上的测试结果及与国外文献报道的比较研究表明：本文所提出的改进型CVP 架构及相应算法在精度和效率上兼有良好表现.     

一种关于目标罚参数的精确罚函数法

罚函数法是一种将约束优化问题转化为无约束问题的重要方法.对于一般的约束优化问题,通过加入新参数,给出了一种改进的精确罚函数和这种罚函数的精确罚定理证明,提出了求解这种罚函数的算法.实验表明该算法是有效的.     

Penalty Approximation for Dynamical Systems Submitted to Multiple Non-Smooth Constraints

We consider discrete mechanical systems subject to perfect unilateral constraints. Moreau's impact law uses the decomposition of the velocity on the normaland tangent cones to the set of admissible positions at the impact point, and it iswell-known that this gives the only possible rule in the case of a singleperfect unilateral constraint. In the multi-constraint case, there areother possible energetically and geometrically consistant impact laws. Weshow here that in a number of cases, the limiting behavior of impact asits rigidity tends to infinity is given by Moreau's rule, i.e., in a more mathematical language, we justify this impact law by a penalty approach.First we describe the penalty method, then we apply it to amulti-constraint model problem. We choose an overdamped approximationand we obtain Moreau's rule for inelastic shocks in the limit. Finally,we present the computational drawbacks of the penalty method.     

微粒群算法在非线性约束优化中的应用

该文将微粒群算法(PSO)应用于非线性约束优化问题的求解,提出了一种求解非线性约束优化问题的新算法,数值试验表明该算法具有很强的全局寻优能力。     

Dynamic Terrain Visualization Based on ROAM and OGRE

Terrain Visualization is an important part of visualization systems of battlefield, and the visualization of dynamic terrain is also important for dynamic battle environment. In this paper, special attention has been paid on real-time optimally adapting meshes （ROAM） algorithm, which is a candidate for dynamic terrain, and its mesh representation, mesh continuity algorithm and error metrics are discussed. The DEXTER-ROAM algorithm is discussed and analyzed. By revising the mesh representation of ROAM, a dynamic ROAM algorithm based on partial-regular grid is established. By introducing transition region, mesh discontinuity of dynamic partial-regular grid is resolved. Error metric blocks are removed for computation complexity and culling blocks are introduced to accelerate view frustum culling. The algorithm is implemented in a 3D rendering engine called OGRE. In the end, an example of dynamic crater is given to examine the dynamic ROAM algorithm.     

基于分段层近法的SMO参数选择

传统的支撑向量机(SVM)训练速度非常慢,使用RBF核的序列最小优化(SMO)是有效的SVM改进算法.综合网格法和双线形法的优点,提出分段层近法选择参数惩罚因子C和核参数σ2.同时用来训练二维数据,实验证明,SMO算法与传统的SVM算法都使用该法选定参数,在推广识别率方面为同一水平的情况下,运行速度有很大的提高.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

求解约束优化问题的改进灰狼优化算法

针对基本灰狼优化(GWO)算法存在求解精度低、收敛速度慢、局部搜索能力差的问题,提出一种改进灰狼优化(IGWO)算法用于求解约束优化问题。该算法采用非固定多段映射罚函数法处理约束条件,将原约束优化问题转化为无约束优化问题,然后利用IGWO算法对转换后的无约束优化问题进行求解。在IGWO算法中,引入佳点集理论生成初始种群,为算法全局搜索奠定基础;为了提高局部搜索能力和加快收敛,对当前最优灰狼个体执行Powell局部搜索。采用几个标准约束优化测试问题进行仿真实验,结果表明该算法不仅克服了基本GWO的缺点,而且性能优于差分进化和粒子群优化算法。     

An efficient approach for the S‐shaped penalty function

Interval goal programming (IGP) with a marginal penalty function (PF) was first proposed by Charnes and Collomb in 1972, and further improved by Kvanli and other researchers. Recently, Lu and Chen proposed an efficient logarithmic method to formulate IGP with an S‐shaped PF. However, their method requires adding many binary variables when the problem size becomes large, which increases the computational burden in the solution process. This study proposes an efficient approach for the S‐shaped PF. The arbitrary PF frequently appears in the fields of business and industry. However, none of the previous approaches have addressed arbitrary PFs without adding binary variables. The proposed approach can be easily extended to formulate an arbitrary PF in which binary variables are no longer required, regardless of the number of break points. The proposed method can improve the efficiency of IGP for solving large size management and decision problems in considering PFs. In order to demonstrate the correctness, usefulness of the proposed model, illustrative examples are provided.