基于改进STA的含风电场电力系统动态经济调度

含风电场电力系统动态经济调度问题具有高维度、非线性、多约束的特点,同时需要考虑风电的随机性。针对上述问题提出一种基于镜像变换和动态坐标变换的状态转移算法(MTSTA),并使用一种新型的处理约束的修复方法配合罚函数对约束条件进行处理。镜像变换和动态坐标变换的加入丰富了候选解的多样性,提高了算法的收敛速度和寻优精度。用含一座风电场的10机系统作为算例,与其它几种算法进行对比,仿真结果验证了上述算法求解含风电场电力系统动态经济调度问题的优越性。     

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.     

遗传模拟退火算法在约束求解中的应用

将遗传模拟退火算法应用于约束求解中 ,提高了约束系统求解的鲁棒性和效率 .与 Newton- Raphson数值方法相比 ,由于遗传模拟退火算法是一种单纯的数值迭代方法 ,不涉及到矩阵求逆 ,因此克服了 Newton- Raphson法对初始值敏感的缺点 ,具有很强的鲁棒性 ;与其他利用 BFGS的优化算法相比 ,由于遗传模拟退火算法是在一个初始的解空间中搜索所有可能的解 ,因此克服了 BFGS优化算法对良约束多解情况只能求出一个解的缺点 ;由于遗传模拟退火算法是将约束问题转化为优化问题后才进一步求解 ,因此其可以处理过约束一致和欠约束的问题     

基于混合差分进化和alpha约束支配处理的多目标优化算法

针对约束多目标优化问题, 提出了一种基于混合差分进化和alpha约束支配处理的优化算法. 算法在用约束水平度对个体满足约束条件的程度进行定量化表达的同时融入支配关系. 在初期放宽约束水平度, 利用不可行解所携带的有用信息, 增加种群多样性, 在后期紧缩约束水平度, 控制不可行解的比例, 朝可行域方向进化. 同时, 将动态单纯形交叉算子和差分进化结合起来构成一种混合差分进化算法, 提高算法的探索和开发能力. 对6个典型测试函数求解的结果显示, 本文算法无论是在收敛性方面还是解集分散性方面, 与其它算法相比具有很大的优势.     

求解带约束优化问题的混合式多策略萤火虫算法

目前多目标优化算法主要针对如何处理多个目标之间的冲突,对于如何处理约束考虑较少,鉴于此,提出一种求解带约束优化问题的混合式多策略萤火虫算法(HMSFA-PC).首先,提出一种改进的动态罚函数策略对约束优化问题进行预处理,将其转换为非约束优化问题;其次,对萤火虫算法本身进行改进,采用Lévy flights搜索机制有效地增大搜索范围;接着,引入随机扩张因子改进算法吸引模型,使种群突破束缚,有效避免早熟收敛,提出自适应维度重组机制,根据不同迭代时期选择差异性较大的个体进行信息交互、相互学习.为检验算法处理无约束优化问题的性能,将其在基准测试函数上与部分典型算法进行比较;为检验算法处理约束优化问题的性能,将其在实际约束测试问题中与一些顶尖约束求解算法进行比较.结果表明,HMSFA-PC在处理无约束优化问题时具有收敛速度快、收敛精度高等优势,并且在动态罚函数的协作下求解实际约束优化问题时仍具有良好的优化性能.     

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.     

求解约束优化问题的动态邻域粒子群算法*

粒子群算法(PSO)求解约束优化问题存在较严重的早熟收敛现象,为了有效抑制早熟收敛,提出了基于改进的约束自适应方法的动态邻域粒子群算法(IPSO)。算法采用动态邻域策略提高算法的全局搜索能力,设计了一种改进的自适应约束处理方法,根据迭代代数线性增加搜索偏向系数,在早期偏向于搜索可行解,在后期偏向于搜索最优解,并引入序列二次规划增强算法的局部搜索能力。通过基准测试函数实验对比分析,表明该算法对于约束优化问题具有较好的全局收敛性。     

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.     

基于MLD模型的预测控制可行性与约束优先级研究

混合逻辑动态(MLD)框架为处理约束预测控制不可行和优先级问题提供了新的方法,但其优化算法求解计算量大,求解时间长.通过在目标函数中引入新的惩罚项改进优化算法,将混合整数二次规划(MIQP)问题转化为二次规划(QP)进行求解,减少了计算工作量,提高了求解效率.通过在Wood-Berry塔上的仿真分析,获得了良好的控制性能,分析结果表明了该方法的有效性.     

基于Oracle罚方法的混合约束差分进化算法

为有效求解复杂约束优化问题,提出了一种基于Oracle的混合约束差分进化算法OBHSaDE.在OBHSaDE算法中,首先对Oracle罚方法进行了改进,并符合约束优化问题的求解要求.利用改进后的Oracle罚方法来快速找到问题的可行域,借助无约束优化算法SaDE能对可行域进行有效搜索,利用序列二次规划的超线性的收敛速度来减少评估次数和提高解的质量.仿真结果表明,改进算法不仅减少了评估次数、提高了解的质量,且具有很好的鲁棒性,还具有较少的用户参数,提高了算法的实用性.OBHSaDE是求解约束优化问题的一种具有竞争力的新方法.     

A hybrid approach to constrained global optimization

In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to.     

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。     

啤酒生产调度数学模型及其优化

针对啤酒企业生产人工调度效果不理想的问题,建立了啤酒生产调度数学模型,并研究了此类间歇工业调度问题的优化方法.根据啤酒生产流程特点,将整个啤酒生产划分为糖化区、过滤包装区,分别建立相应的生产调度数学规划模型,并通过蚁群优化算法求解此类调度问题.该优化调度方案在企业中的应用结果表明,通过蚁群算法对建立的啤酒调度模型进行优化,该方法具有良好的鲁棒性与实用性,可为生产管理人员提供方便快捷的优化解决方案.     

求解双层规划优化问题的层次风驱动优化算法

针对双层规划的求解问题, 提出一种层次风驱动优化算法. 初始化上层优化变量后, 首先对下层规划进行求解, 满足约束条件的同时, 更新下层规划中的空气质点速度和位置; 然后, 利用风驱动优化算法对上层规划问题进行求解; 最后, 在优化解集合中, 选择上下层规划目标值次序之和最小的解作为最终优化解. 实验结果表明, 所提出的层次风驱动算法是一种有效的求解双层规划问题的方法.

     

Self-adaptive constrained artificial bee colony for constrained numerical optimization

The artificial bee colony is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem, and later, it was extended to constrained design problems as well. This paper describes a self-adaptive constrained artificial bee colony algorithm for constrained optimization problem based on feasible rule method and multiobjective optimization method. The employed bee colony severs as the global search engine for each population based on feasible rule. Then, the onlooker bee colony can explore the new search space based on the multiobjective optimization. In order to enhance the convergence rate of the proposed algorithm, a self-adaptive modification rate is proposed to make the algorithm can change many parameters. To verify the performance of our approach, 24 well-known constrained problems from 2006 IEEE congress on Evolution Computation (CEC2006) are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, state-of-the-art approaches in terms of the quality of the resulting solutions from literature.     

Smooth twin support vector regression

Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel planes, i.e., ε-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal solution since its objective function is positive semi-definite and the lack of complexity control. In order to address this shortcoming, we develop a novel SVR algorithm termed as smooth twin SVR (STSVR). The idea is to reformulate TSVR as a strongly convex problem, which results in unique global optimal solution for each subproblem. To solve the proposed optimization problem, we first adopt a smoothing technique to convert the original constrained quadratic programming problems into unconstrained minimization problems, and then use the well-known Newton–Armijo algorithm to solve the smooth TSVR. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-world benchmark datasets.     

Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.     

基于改进DE-NMPC的酸碱中和反应pH值控制

将一种改进的基于差分进化算法的非线性预测控制应用到酸碱中和反应pH值控制系统. 算法充分利用滴定曲线模型, 指导优化过程搜索的初始化空间. 同时, 在变异和选择等操作中改进了差分进化算法, 解决了一类有边界约束的非线性优化问题. 在发酵罐实验装置中进行了测试实验, 取得了较好的效果.     

A hybrid cooperative search algorithm for constrained optimization

Many engineering design problems can be formulated as constrained optimization problems which often consist of many mixed equality and inequality constraints. In this article, a hybrid coevolutionary method is developed to solve constrained optimization problems formulated as min–max problems. The new method is fast and capable of global search because of combining particle swarm optimization and gradient search to balance exploration and exploitation. It starts by transforming the problem into unconstrained one using an augmented Lagrangian function, then using two groups to optimize different components of the solution vector in a cooperative procedure. In each group, the final stage of the search procedure is accelerated by via a simple local search method on the best point reached by the preceding exploration based search. We validated the effectiveness and robustness of the proposed algorithm using several engineering problems taken from the specialised literature.     

Optimum layout design of onshore wind farms considering stochastic loading

Renewable energy technologies are developing rapidly, while in the last decade great interest is encountered in the use of wind energy, especially due to the energy crisis and serious environmental problems appeared from the use of fossil fuels and therefore a large number of wind farms have been installed around the world. On the other hand the ability of nature inspired algorithms to efficiently handle combinatorial optimization problems was proved by their successful implementation in many fields of engineering sciences. In this study, a new problem formulation for the optimum layout design of onshore wind farms is presented, where the wind load is implemented using stochastic fields. For this purpose, a metaheuristic search algorithm based on a discrete variant of the harmony search method is used for solving the problem at hand. The farm layout problem is by nature a constrained optimization problem, and the contribution of the wake effects is significant; therefore, in two formulations presented in this study the influence of wind direction is also taken into account and compared with the scenario that the wake effect is ignored. The results of this study proved the applicability of the proposed formulations and the efficiency of combining metaheuristic optimization with stochastic wind loading for dealing with the problem of optimal layout design of wind farms.