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1.
This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper.This approach offers promise for the portfolio problems in practice.  相似文献   

2.
This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment (TUC) problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modem power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies, which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm (IA) is used as the initial solution of the tabu search (TS) algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.  相似文献   

3.
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in- troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.  相似文献   

4.
The generalized complementarity problem includes the well-known nonlinear complementarity problem and linear complementarity problem as special cases.In this paper, based on a class of smoothing functions, a smoothing Newton-type algorithm is proposed for solving the generalized complementarity problem.Under suitable assumptions, the proposed algorithm is well-defined and global convergent.  相似文献   

5.
This paper presents a quadratic programming method for optimal multi-degree reduction of Bézier curves with G1-continuity. The L2 and l2 measures of distances between the two curves are used as the objective functions. The two additional parameters, available from the coincidence of the oriented tangents, are constrained to be positive so as to satisfy the solvability condition. Finally, degree reduction is changed to solve a quadratic problem of two parameters with linear constraints. Applica  相似文献   

6.
In this paper,a discussion on the new polynomial-time algorithm for linearprogramming as proposed by Karmarkar.N.is presented.The problem is solved when aninitial feasible solution is unknown.For the case where the optimum value of the objectivefunction is unknown,the reasonableness and feasibility of the sliding objective functionmethod are proved.And a method of modifying the parameters is put forward.  相似文献   

7.
This paper presents a method to reconstruct symmetric geometric models from point cloud with inherent symmetric structure. Symmetry types commonly found in engineering parts, i.e., translational, reflectional and rotational symmetries are considered. The reconstruction problem is formulated as a constrained optimization, where the objective function is the sum of squared distances of points to the model, and constraints are enforced to keep geometric relationships in the model. First, the explicit representations of symmetric models are presented. Then, by using the concept ofparameterized points (where the coordinate components are represented as functions rather than constants), the distances of points to symmetric models are deduced. With these distance functions, symmetry information, for both 2D and 3D models, is uniformly represented in the process of reconstruction. The constrained optimization problem is solved by a standard nonlinear optimization method. Owing to the explicit representation of symmetry information, the computational complexity of our method is reduced greatly. Finally, examples are given to demonstrate the application of the proposed method.  相似文献   

8.
This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value,the model adopts LM (Leven-berg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.  相似文献   

9.
This paper presents a quadratic programming method for optimal multi-degree reduction of Bézier curves with G1-continuity. The L2 and l2 measures of distances between the two curves are used as the objective functions. The two additional parameters, available from the coincidence of the oriented tangents, are constrained to be positive so as to satisfy the solvability condition. Finally, degree reduction is changed to solve a quadratic problem of two parameters with linear constraints. Applica- tions of degree reduction of Bézier curves with their parameterizations close to arc-length parameterizations are also discussed.  相似文献   

10.
Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.  相似文献   

11.
针对多目标无约束0—1二次规划问题,提出一种文化基因算法。该算法采用基于分解的多目标演化算法框架,能够获得分布均匀的非占优解;同时,采用一种简单、有效的禁忌搜索,能够利用更多问题相关的信息,获得质量更优的非占优解。该算法在优化的过程中能够动态地平衡多样性与收敛性。实验结果证明该算法能够很好地求解多目标无约束0-1二次规划问题,并且性能优于目前求解该问题较先进的算法。  相似文献   

12.
INTRODUCTIONAntcolonyalgorithms (Hertz ,etal.,2 0 0 0 ) ,investigatedsystematicallyatfirstinDorigo’sPh .D .dissertation ( 1 992 )astheimi tationofthefood seekingbehaviorinantsociet ies,haveattractedthegreatattentionofre searchersincomprehensivefieldsofsystemopti mizat…  相似文献   

13.
研究目的:为改善实际工程结构在不确定性条件下的多性能指标,提供一种高效的区间多目标优化方法。创新要点:建立一个目标和约束均为区间不确定性参数函数的区间约束多目标优化模型,提出并实现基于径向基函数、区间分析和非支配排序遗传算法(NSGA-II)的区间多目标优化算法。研究方法:首先,利用区间序关系将每个区间目标转换为同时优化其中点和半径的确定性双目标,利用区间可能度法将区间约束转换为确定性约束,并在此基础上,利用加权法和罚函数法将每个区间目标的约束优化问题转换为相应的无约束优化问题;然后,利用拉丁超立方实验设计和有限元分析构建预测各待优化结构性能指标值的径向基函数;最后,将径向基函数、区间分析法与NSGA-II相结合,快速求出转换后确定性无约束多目标优化问题的所有Pareto最优解,并通过考虑材料不确定性的高速压力机滑块机构设计实例验证该方法的有效性。重要结论:目标和约束均为不确定性参数函数的区间多目标优化模型能有效反映实际工程中同时改善结构多性能指标的需求。基于径向基函数、区间分析和NSGA-II相结合的区间多目标优化算法将传统区间优化模型求解中的嵌套优化过程简化为单层遗传优化过程,大大提高了求解效率,并可获得多目标优化问题的所有Pareto最优解。  相似文献   

14.
为了综合考虑锅炉燃烧优化问题中锅炉效率与NOx排放2个目标,提出了一种新的基于免疫细胞亚群的多目标优化算法ICSMOA.算法定义了亚群划分算子与免疫耐受算子,亚群划分可以很方便地表达偏好,免疫耐受则能保证解的分布性.ICSMOA的运行结果为一组Pareto最优解,而传统的加权法的运行结果为一个不能判断Pareto占优与否的解.与多次运行加权法获得的结果相比,所提算法的运行结果优于加权法.另外,运行ICS-MOA所获得的Pareto前沿不同于经典的多目标优化算法,它可以输出更多的满足决策者偏好的解,从而更适合于工业应用.  相似文献   

15.
基于偏好多目标蜂群算法的过热汽温控制系统优化(英文)   总被引:1,自引:0,他引:1  
为了将决策者的偏好综合到多目标问题求解过程中,提出了一种偏好多目标蜂群优化算法PMABCA.在PM ABCA中,给出了一种新的偏好距离计算方法,基于非支配等级与偏好距离定义了适应度分配函数,并引入了归档集用于非支配解的存储.为了清除非支配集中多余的解,提出了改进的偏好拥挤距离算子.针对经典函数优化问题的计算结果表明,PMABCA可以在输出完整Pareto前端的基础上,确保输出大量符合偏好的最优解.将PMABCA应用于过热汽温控制系统PID参数优化问题,仿真结果表明,新算法的结果更便于决策者做出合理决策.  相似文献   

16.
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved. Project (No. 9845-005) supported by National High-Tech. Research & Development Plan, China  相似文献   

17.
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.  相似文献   

18.
基于遗传算法的火电单元机组多目标优化协调控制   总被引:1,自引:0,他引:1  
作者提出了一种基于遗传算法的火电单元机组多目标优化协调控制策略。该策略通过改进的遗传算法进行多目标优化求解机组最优稳态控制量以得到最优设定值,从而完成多目标优化协调控制任务。改进的遗传算法采用十进制编码,规范化几何秩选择,混合交叉及均匀变异。仿真结果表明,在不同的运行目标下控制量的最优适应度函数都能快速收敛,遗传算法为多目标优化协调控制提供了有效的途径。  相似文献   

19.
In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.  相似文献   

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