一种求解同等并行机调度的混合量子衍生进化规划算法

针对带顺序相关建立时间的同等并行机调度问题的求解,提出一种新的混合量子衍生进化规划算法.该算法通过定义新的量子个体来表示调度问题中的工件排序,并定义了针对调度问题的量子旋转角,使个体向更好的解靠近.同时,针对并行机问题本身,改进了个体的编码方式和新的变异方法.为了验证算法的有效性和收敛性,采用不同规模的算例进行仿真实验.结果表明,即使在小种群情况下,算法所得解均优于基本进化规划求得的解.     

k-着色问题及其均场退火求解算法

均场退火方法既可以看作是一种新的神经网络计算模型,又可视为是对模拟退火的重大改进.该文把具有相邻约束的多层通孔最小化问题转换为更具广泛意义的k-着色问题,并提出了k-着色问题的均场退火求解算法.算法在线段相交图模型的基础上,提出了相邻矩阵和交叠矩阵等概念,并利用换位矩阵,将问题映射为相应的神经网络,再构造了该问题的能量函数.能量函数中的目标项、违背交叠约束的惩罚项、违背相邻约束的惩罚项和神经元归一化处理保证了网络能够求解到一个合法解.实验结果表明,这是一个有效的算法.     

基于Hopfield神经网络的作业车间生产调度方法

该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证神经网络稳态输出为可行的作业车间调度方案.     

基于NSGA2算法的并行机多目标调度问题研究

针对并行机多目标调度问题,以完工时间和总延迟时间最小为目标函数建立了数学模型,从而将具有解决复杂组合优化问题的非劣排序遗传算法NSGA2应用于求解多目标并行机调度问题。文中详细描述了用NSGA2算法求解并行机调度问题的步骤,并通过Matlab仿真,表明YhqNSGA2算法求解多目标并行机调度问题的可行性和有效性。     

并行流程车间调度问题及其概率学习进化算法

并行Flowshop调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题.针对最小化最大完工时间目标函数,建立了一般并行Flowshop调度问题的整数规划模型.鉴于问题的求解复杂性,设计了基于概率学习的求解算法.对随机生成的测试问题进行求解,实验结果显示出该算法求解并行Flowshop调度问题的良好潜能.     

蚁群算法求解非等同并行机调度问题的研究

针对非等同并行机服务调度问题,以机场除冰调度服务为背景并以最小化旅客延误数为目标,提出了一种改进的蚁群算法。该算法根据调度模型的特点,充分考虑模型的约束条件并运用了一种改进的信息素更新策略求解并行机调度问题。仿真结果表明,改进的蚁群算法收敛速度快且结果较优,明显优于FIFO算法,适合求解非等同并行机调度问题。     

复杂并行机调度问题基于分解的优化算法

针对纺织生产过程中广泛存在的带特殊工艺约束的大规模并行机调度问题，提出了一种基于分解的优化算法。首先将原调度问题分解为机台选择和工件排序两个子问题，然后针对机台选择子问题提出一种进化规划算法，并采用一种具有多项式时间复杂度的最优算法求解工件排序子问题，以得到问题特征信息（即每台机器对应拖期工件数的最小值），该问题特征信息用以指导进化规划算法的迭代过程。不同规模并行机调度问题的数值计算结果及实际制造企业应用效果表明，本文提出的算法是有效的。     

基于粗糙规划的不确定加工时间的并行机调度

针对并行机调度中的不确定工件加工时间,提出用粗糙变量表示不确定量,并由此建立该问题的粗糙期望值规划模型.提出一种应用于调度问题的进化规划算法,改进了针对并行机问题的编码方式和变异方法.采用粗糙模拟的方法计算个体的适应值,即粗糙期望估计值,并加以不同规模的算例进行仿真实验.仿真结果表明,改进进化规划算法得到的解优于遗传算法得到的解.     

基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度和经济排放联合调度问题

电力生产装置运行中各种燃料的成本逐步增加,需要最小化成本函数以求解此类复杂经济负荷调度问题.鉴于此,提出一种基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度(economic load dispatch, ELD)问题和经济排放联合调度(combined economic emission dispatch, CEED)问题.为了提高蚱蜢算法(grasshopper optimization algorithm, GOA)性能,提出一种改进的混合蚱蜢算法(hybrid grasshopper optimization algorithm, HGOA),将重力搜索算子和鸽群搜索算子-地标算子加入GOA中,增强算法的搜索能力,平衡算法的勘探和开发.同时,为了更好地解决ELD和CEED问题中的约束问题,提出6个惩罚函数,包括2个V型函数、反正切函数、反正弦函数、线性函数和二次函数,并使用动态惩罚策略代替传统的固定值惩罚策略.选取3个ELD问题案例和4个CEED问题案例验证所提出方法的有效性,实验结果表明, HGOA相较于其他元启发式算法在求解质量上表现更好,且动态惩罚策略比固定值惩罚策略效果更...     

求解考虑机器调整时间的并行机分批优化调度问题

基于目前车间调度问题是以单个或整批进行生产加工的并行机调度模型已不再符合实际工况下的车间生产。提出以最小化最大完工时间为优化目标,对遗传差分进化混合算法,灰狼差分进化混合算法进行了比较。为提高加工工件进行分批及分批之后子批的分配与排序效率,该问题是对不同规模的经典并行机调度问题进行求解并展示两种算法的求解,证明了灰狼差分进化混合算法在寻优性能上优于遗传差分进化混合算法,不仅具有更好的解的稳定性,而且具有更高的寻优精度。     

Constrained Dimensionality Reduction Using a Mixed-Norm Penalty Function with Neural Networks

Reducing the dimensionality of a classification problem produces a more computationally-efficient system. Since the dimensionality of a classification problem is equivalent to the number of neurons in the first hidden layer of a network, this work shows how to eliminate neurons on that layer and simplify the problem. In the cases where the dimensionality cannot be reduced without some degradation in classification performance, we formulate and solve a constrained optimization problem that allows a trade-off between dimensionality and performance. We introduce a novel penalty function and combine it with bilevel optimization to solve the constrained problem. The performance of our method on synthetic and applied problems is superior to other known penalty functions such as weight decay, weight elimination, and Hoyer's function. An example of dimensionality reduction for hyperspectral image classification demonstrates the practicality of the new method. Finally, we show how the method can be extended to multilayer and multiclass neural network problems.     

Optimal routing in multiple I/O data network using neural network with perturbed energy function

An optimal routing problem in multiple I/O data network is one of the most important problems related on the performance of the network basically, and is formulated as a constrained nonlinear optimization problem. When solving the problem considered using neural networks, we may obtain local minima, rather than global minimum, because the problem has multimodals. In this paper, we introduce a perturbed energy function into the neural network based on a penalty method to solve the multimodal nonlinear optimization problem.     

求解约束优化问题的人工鱼群算法

在利用人工鱼群算法求解约束问题时,处理好约束条件是取得好的优化效果的关键。引入了半可行域的概念,并结合人工鱼群算法(ArtificialFish-SwarmAlgorithm,AFSA)本身的特点,设计了基于竞争选择和惩罚函数的适应度函数,从而得到了一个利用ASFA算法求解约束优化问题的新的进化算法。实验证明了算法的有效性。     

A new method based on adaptive volume constraint and stress penalty for stress-constrained topology optimization

This paper focuses on the stress-constrained topology optimization of minimizing the structural volume and compliance. A new method based on adaptive volume constraint and stress penalty is proposed. According to this method, the stress-constrained volume and compliance minimization topology optimization problem is transformed into two simple and related problems: a stress-penalty-based compliance minimization problem and a volume-decision problem. In the former problem, stress penalty is conducted and used to control the local stress level of the structure. To solve this problem, the parametric level set method with the compactly supported radial basis functions is adopted. Meanwhile, an adaptive adjusting scheme of the stress penalty factor is used to improve the control of the local stress level. To solve the volume-decision problem, a combination scheme of the interval search and local search is proposed. Numerical examples are used to test the proposed method. Results show the lightweight design, which meets the stress constraint and whose compliance is simultaneously optimized, can be obtained by the proposed method.     

On solving constrained optimization problems with neural networks:a penalty method approach

Deals with the use of neural networks to solve linear and nonlinear programming problems. The dynamics of these networks are analyzed. In particular, the dynamics of the canonical nonlinear programming circuit are analyzed. The circuit is shown to be a gradient system that seeks to minimize an unconstrained energy function that can be viewed as a penalty method approximation of the original problem. Next, the implementations that correspond to the dynamical canonical nonlinear programming circuit are examined. It is shown that the energy function that the system seeks to minimize is different than that of the canonical circuit, due to the saturation limits of op-amps in the circuit. It is also noted that this difference can cause the circuit to converge to a different state than the dynamical canonical circuit. To remedy this problem, a new circuit implementation is proposed.     

An exact penalty function algorithm for solving general constrained parameter optimization problems

An exact penalty function type of algorithm is proposed to solve a general class of constrained parameter optimization problems. The proposed algorithm has the property that any solution obtained by it will always satisfy the problem constraints, and that it will obtain a solution to the constrained problem, within a given specified tolerance, by solving a single unconstrained problem, i.e. it is not necessary to solve a sequence of unconstrained optimization problems. The algorithm applies a modification of Rosenbrock's (Rosenbrock, 1960) polynomial boundary penalty function, and a negative exponential penalty function with moving parameters, to modify the objective function in the neighborhood of the constrained region; a robust unconstrained algorithm (Davison and Wong, 1975) is then used to solve the resulting unconstrained optimization problem. Some standard test functions are included to show the performance of the algorithhm. Application of the algorithm is then made to solve some computer-aided design problems occurring in the area of control system synthesis.     

非线性规划问题的极大熵多目标粒子群算法

结合非线性规划的约束条件构造了一个新的极大熵函数,利用该函数将问题转化成了两个目标的多目标优化问题.通过对违反约束动态的进行惩罚,提出了一种新的极大熵多目标粒子群算法.该方法能有效的保持群体中不可行解的一定比例,从而增加了群体的多样性,而且避免了传统的过度惩罚缺陷,使群体更好地向最优解逼近.计算机仿真表明,该算法对非线性规划问题求解是非常有效的.     

Practical application of a penalty function approach to constrained minimax optimization

A practical, penalty function approach to solving constrained minimax problems is applied here. In essence, this approach reformulates the constrained minimax problem as an unconstrained minimax problem. A recently proposed optimization algorithm called grazor search is used to solve the reformulated unconstrained minimax problem. The proposed approach can handle inequality constraints-parameter constraints in particular. A practical transmission-line filter example with parameter constraints illustrates the results.     

A new gradient-based neural network for solving linear andquadratic programming problems

A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve linear and quadratic programming problems. In particular, a new function F(x, y) is introduced into the energy function E(x, y) such that the function E(x, y) is convex and differentiable, and the resulting network is more efficient. This network involves all the relevant necessary and sufficient optimality conditions for convex quadratic programming problems. For linear programming and quadratic programming (QP) problems with unique and infinite number of solutions, we have proven strictly that for any initial point, every trajectory of the neural network converges to an optimal solution of the QP and its dual problem. The proposed network is different from the existing networks which use the penalty method or Lagrange method, and the inequality constraints are properly handled. The simulation results show that the proposed neural network is feasible and efficient.     

Constraints Satisfaction through Recursive Neural Networks with Mixed Penalties: a Case Study

This paper investigates an industrial assignment problem. It is modelized as a constraint satisfaction problem of large size with linear inequalities and binary variables. A new analog neuron-like network is proposed to find out feasible solutions to problems having several thousands of 0/1 variables. The approach developed in this paper is based on mixed-penalty functions: exterior penalty functions together with interior penalty functions. Starting from a near-binary solution satisfying each linear inequality, the network generates trial solutions located outside or inside the feasible set, in order to minimize an energy function which measures the total binary infeasibility of the system. The performances of the network are demonstrated on real data sets.