基于演化算法实现多目标优化的岛屿迁徙模型

多目标演化算法(MOEA)利用种群策略，尽可能地找出多目标问题的Pareto最优集供决策者选择，为决策者提供了更大的选择余地，与其它传统的方法相比有了很大的改进．但提供大量选择的同时，存在着不能为决策者提供一定的指导性信息，不能反映决策者的偏好，可扩展性差等问题．本文提出了一个新的多目标演化算法(MOEA)计算模型…岛屿迁徙模型，该模型体现了一种全新的多目标演化优化的求解思想，对多目标优化问题的最优解集作了新的定义．数值试验结果表明，岛屿迁徙模型在求解MOP时有效地解决了以上问题，并且存在进一步改进的潜力．     

多目标分散决策的目标规划模型和玻尔兹曼机解法

研究建立了分散组织结构下多目标分散决策的目标规划模型，根据随机神经网络－－玻尔兹曼机的基本原理，提出了求解该问题的一种新方法。示例的仿真结果表明该方法是非常有效的。     

计算机动态取证的数据分析技术研究

本文针对计算机动态取证的数据分析阶段面临的问题，提出将数据挖掘技术应用于计算机动态取证的海量数据分析中，给出了基于数据挖掘的计算机动态取证系统模型，提高动态取证中数据分析的速度、分析的准确性和分析的智能性，解决动态取证中的实用性、有效性、可适应性和可扩展性问题。     

软件算求解分布式多工厂单件制造业的提前／拖期生产计划问题

描述了分布式多工厂单件制造企业准时化生产计划问题，以实现最小化提前／拖期惩罚费用，生产成本，产品运输费用之和为目标建立0－1规划数学模型，设计了基于模糊规则量化的方法求解模糊决策，并将模糊决策嵌入到遗传算法中的软计算方法求解模型，使得算法具有比分枝定界快速的寻找优解的能力以及更广泛的适应范围，结果表明了该模型和算法的有效性和应用潜力。     

计算机动态取证系统模型研究

在犯罪事件发生后对犯罪行为进行事后的取证,存在着证据的真实性、有效性和及时性问题。本文提出将取证技术结合到防火墙、入侵检测系统中,对所有可能的计算机犯罪行为进行实时的动态取证,重点研究了基于数据挖掘的多智能代理动态取证系统模型以及基于该模型下的数据获取模块和数据分析模块。     

基于Windows 平台的动态取证系统

针对目前一些动态取证模型的不足,在分布式网络取证模型的基础上设计了一个基于Windows平台的动态取证系统,能够实现网络中的计算机作为作案目标和作案工具双重角色时的取证,具有实时获取多种数据源、取证过程隐秘、取证分析算法可扩展等特点。介绍了动态取证系统中各功能模块设计,并阐述了系统设计中涉及到的关键技术,最后通过模拟测试表明该系统能够在Windows网络下实现动态取证。     

基于多Agent的网络取证自适应技术研究

在分析目前网络取证系统的不足和多Agent自适应技术特点的基础上，本文提出并建立了自适应网络取证模型，针对其中的检测与决策取证两个关键Agent工作原理进行了详细探讨，并就其自适应性进行了论述。     

基于风险指标的干旱期水资源系统运行优化分析

根据干旱期水资源系统运行性能分析特征，建立了基于供水可靠性最大、供水破坏恢复能力最强以及单一时段破坏深度最小等风险指标，同时考虑了城市和农业供水优先权问题的多目标混合整数规划模型．在实例研究中，运用多目标决策法中的协调规划原理，通过评价任一目标点与理想点的欧氏距离，获得多目标问题的最佳权衡解．计算结果表明，该多目标混合整数规划模型对解决需要考虑供水优先权的干旱期水库运行策略优化问题是可行而有效的．     

基于新型双目标模型的约束优化进化算法

利用双目标模型求解约束优化问题时,由于它们的最优解集并不相等,因此需要增加特殊机制确保求解双目标问题的算法收敛到原问题的最优解.为克服这一缺点,本文首先将约束优化问题转化为新的双目标优化模型,并证明了新模型的最优解集与原问题的最优解集相等.其次,以简单的差分进化为搜索算法,基于多目标Pareto支配关系的非支配排序为选择准则,提出了求解新模型的差分进化算法.最后,用10个标准测试函数的数值试验说明了新模型及求解算法的有效性.     

供应链中供应商选择决策方法

为了克服传统供应商选择过程中面向单一解的局限性,更好地解决供应链中供应商选择的多目标优化问题,达到费用和效益最佳,以产品价格、质量、交货可靠性和交货提前期为评估指标,建立了供应商选择多目标优化模型.首次将SPEA2算法应用于供应商选择问题,构造了适合该模型特征的SPEA2算法求解过程,可针对多个求解目标获得一组均衡解.模拟算例中一次得到多组有效解,表明所建立模型及所用方法是有效、可行的,它为制造企业获得有多种解决方案的供应商选择问题提供了可参考的模型和求解算法.     

Extension of dynamic programming to nonseparable dynamic optimization problems

The use of dynamic programming is extended to a general nonseparable class where multiobjective optimization is used as a separation strategy. The original nonseparable dynamic optimization problem is first embedded into a separable, albeit multiobjective, optimization problem where multiobjective dynamic programming using the envelope approach is used as a solution scheme. Under certain conditions, the optimal solution of the original nonseparable problem is proven to be attained by a noninferior solution.     

Interactive multiobjective fuzzy random programming through the level set-based probability model

This paper considers a multiobjective linear programming problem involving fuzzy random variable coefficients. A new fuzzy random programming model is proposed by extending the ideas of level set-based optimality and a stochastic programming model. The original problem involving fuzzy random variables is transformed into a deterministic equivalent problem through the proposed model. An interactive algorithm is provided to obtain a satisficing solution for a decision maker from among a set of newly defined Pareto optimal solutions. It is shown that an optimal solution of the problem to be solved iteratively in the interactive algorithm is analytically obtained by a combination of the bisection method and the simplex method.     

Multiobjective model predictive control

This paper proposes a novel model predictive control (MPC) scheme based on multiobjective optimization. At each sampling time, the MPC control action is chosen among the set of Pareto optimal solutions based on a time-varying, state-dependent decision criterion. Compared to standard single-objective MPC formulations, such a criterion allows one to take into account several, often irreconcilable, control specifications, such as high bandwidth (closed-loop promptness) when the state vector is far away from the equilibrium and low bandwidth (good noise rejection properties) near the equilibrium. After recasting the optimization problem associated with the multiobjective MPC controller as a multiparametric multiobjective linear or quadratic program, we show that it is possible to compute each Pareto optimal solution as an explicit piecewise affine function of the state vector and of the vector of weights to be assigned to the different objectives in order to get that particular Pareto optimal solution. Furthermore, we provide conditions for selecting Pareto optimal solutions so that the MPC control loop is asymptotically stable, and show the effectiveness of the approach in simulation examples.     

Adaptive differential dynamic programming for multiobjective optimal control

An efficient numerical solution scheme entitled adaptive differential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the “optimal” weighting coefficients for various performance indices are time-varying as the system evolves along any noninferior trajectory. Recognizing this prominent feature in multiobjective control, the proposed adaptive differential dynamic programming methodology combines a search process to identify an optimal time-varying weighting sequence with the solution concept in the conventional differential dynamic programming. Convergence of the proposed adaptive differential dynamic programming methodology is addressed.     

一类优化问题的分解算法

讨论一类大规模系统的优化问题,提出一种递阶优化方法．该方法首先将原问题转化为多目标优化问题,证明了原问题的最优解在多目标优化问题的非劣解集中,给出了从多目标优化问题的解集中挑出原问题最优解的算法,建立了算法的理论基础．仿真结果验证了算法的有效性．     

On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem

In this paper, a multiobjective quadratic programming problem fuzzy random coefficients matrix in the objectives and constraints and the decision vector are fuzzy variables is considered. First, we show that the efficient solutions fuzzy quadratic multiobjective programming problems series-optimal-solutions of relative scalar fuzzy quadratic programming. Some theorems are to find an optimal solution of the relative scalar quadratic multiobjective programming with fuzzy coefficients, having decision vectors as fuzzy variables. An application fuzzy portfolio optimization problem as a convex quadratic programming approach is discussed and an acceptable solution to such problem is given. At the end, numerical examples are illustrated in the support of the obtained results.     

Influence diagrams with multiple objectives and tradeoff analysis

Influence diagrams have been important models for decision problems because of their ability to both model a problem rigorously at its mathematical level and depict its high-level structure graphically. Once the structure and numerical details of an influence diagram have been specified, it can be evaluated to determine the optimal decision policy. However, when evaluating multiple objectives, in the past this determination was based on the assumption that utility functions that commensurate the objectives are available. This paper extends the structure and solution algorithm for influence diagrams to allow for the inclusion of noncommensurate objectives using multiobjective tradeoff analysis instead of utility theory. This eliminates the need to specify any preference information before the influence diagram is solved. The proposed multiobjective-based methodology is also useful for decision makers who either do not want to accept the assumptions of utility theory for a particular problem, or are confronted with a problem in which it is neither practical nor viable to construct a utility function. Additionally, this paper establishes the relationship between multiobjective influence diagrams and multiobjective decision trees. This relationship is important because it allows a decisionmaker to utilize the advantages of both representations. An example problem is presented to introduce both the extended multiobjective influence diagram methodology and the relationship linking multiobjective decision trees to multiobjective influence diagrams.     

经费分配中基于多目标优化的遗传规划模型

在传统遗传规划中引入多目标优化原理,探索新的经费分配方法和管理模式,建立了一种多目标优化的非线性遗传规划模型,提出了一种先进的基于正交试验的新型混合遗传算法来求解该问题.对求解过程中的选择算子、交叉算子和变异算子等进行正交试验,得到的种群个体明显优于基本遗传算法的个体.这种基于多目标优化的遗传规划模型能产生精度更高的最优解,通过对经费分配问题的实验验证,得到了较好的结果.     

A Novel Intelligent Multiobjective Simulated Annealing Algorithm for Designing Robust PID Controllers

This paper proposes an intelligent multiobjective simulated annealing algorithm (IMOSA) and its application to an optimal proportional integral derivative (PID) controller design problem. A well-designed PID-type controller should satisfy the following objectives: 1) disturbance attenuation; 2) robust stability; and 3) accurate setpoint tracking. The optimal PID controller design problem is a large-scale multiobjective optimization problem characterized by the following: 1) nonlinear multimodal search space; 2) large-scale search space; 3) three tight constraints; 4) multiple objectives; and 5) expensive objective function evaluations. In contrast to existing multiobjective algorithms of simulated annealing, the high performance in IMOSA arises mainly from a novel multiobjective generation mechanism using a Pareto-based scoring function without using heuristics. The multiobjective generation mechanism operates on a high-score nondominated solution using a systematic reasoning method based on an orthogonal experimental design, which exploits its neighborhood to economically generate a set of well-distributed nondominated solutions by considering individual and overall objectives. IMOSA is evaluated by using a practical design example of a super-maneuverable fighter aircraft system. An efficient existing multiobjective algorithm, the improved strength Pareto evolutionary algorithm, is also applied to the same example for comparison. Simulation results demonstrate high performance of the IMOSA-based method in designing robust PID controllers.     

Interactive multiobjective decision making in environmental systems using sequential proxy optimization techniques (SPOT)

In this paper, a new interactive multiobjective decision-making technique for solving multiobjective optimization problems: the sequential proxy optimization technique (SPOT), is presented. Using this technique, the preferred solution for the decision maker can be derived efficiently from among a pareto optimal solution set by assessing his marginal rates of substitution and maximizing the local proxy preference functions sequentially. Based on the algorithm of SPOT, a time-sharing computer program is also written to implement man-machine interactive procedures. The industrial pollution control problem in Osaka City in Japan is formulated and the interaction processes are demonstrated together with the computer outputs.