Semivectorial Bilevel Optimization Problem: Penalty Approach

We consider a bilevel optimization problem where the upper level is a scalar optimization problem and the lower level is a vector optimization problem. For the lower level, we deal with weakly efficient solutions. We approach our problem using a suitable penalty function which vanishes over the weakly efficient solutions of the lower-level vector optimization problem and which is nonnegative over its feasible set. Then, we use an exterior penalty method.Communicated by H. P. Benson(Formerly Serban Bolintinéanu) Professor, University of New Caledonia, ERIM, Nouméa, New Caledonia. This author thanks the University of Naples Federico II for its support and the Department of Mathematics and Statistics for its hospitality.     

不可微多目标优化

本文首先说明了什么是不呆微多目标优化问题，然后概括性地介绍了多目标优化研究的主要内容，在此基础上，对不可微多目标优化的主要结果和内容加以综述。     

二层多目标最优化问题解集的连通性

研究了二层多目标最优化模型(BLMOP)解集的连通性问题,其中(BLMOP)的上层集值目标函数由下层问题的有效点确定.把(BLMOP)看作成单层的集值函数优化问题,借助集值函数优化问题各种有效解集的连通性的结论,得到了(BLMOP)相应的有效解集连通性的结论.     

On a Gap between Multiobjective Optimization and Scalar Optimization

We give a suitable example to show a gap between multiobjective optimization and single-objective optimization, which solves a problem proposed in Refs. 1–2.     

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. The problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize the objectives of traveling distance and number of vehicles. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW. The proposed HMOEA is featured with specialized genetic operators and variable-length chromosome representation to accommodate the sequence-oriented optimization in VRPTW. Unlike existing VRPTW approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The HMOEA is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, which yields 20 routing solutions better than or competitive as compared to the best solutions published in literature.     

非线性不等式与等式约束的多目标规划问题的区间极大熵方法

利用极大熵方法将带多个非线性不等式约束和多个非线性等式约束的多目标规划问题变为两个非线性不等式约束的单个可微的目标函数优化问题,并结合区间分析知识给出一种新的解决多目标规划问题的区间方法.     

大规模系统的全局优化

本文讨论了可分非凸大规模系统的全局优化控制问题 .提出了一种 3级递阶优化算法 .该算法首先把原问题转化为可分的多目标优化问题 ,然后凸化非劣前沿 ,再从非劣解集中挑出原问题的全局最优解 .建立了算法的理论基础 ,证明了算法的收敛性 .仿真结果表明算法是有效的 .     

About a Gap between Multiobjective Optimization and Scalar Optimization

We explain the reason for the existence of a gap between multiobjective and scalar optimization proposed in Ref. 1, giving a characterization of this gap.     

公交车优化调度中的几个问题的研究

本文将 2 0 0 1 CMCM B题公交车调度的最优化问题分成两个相对独立的问题来讨论 ,由所要运送的乘客数来决定公交公司的最小车辆数 ,由乘客到达来决定发车时刻表 .我们求出了最小车辆数和一个可行的最优时刻表 .     

A General Linea-Quadratic Optimization Problem

A linear-quadratic optimization problem is formulated in a dynamic programming manner. An updating formula for obtaining the solutions to such a problem is provided and illustrated using a few simple examples. This updating formula is also compared to a well-known updating formula for obtaining the inverses of symmetric positive-definite matrices. Numerical results are given.     

半球面上一个几何优化问题

单位球面x2+ y2+ z2=1的赤道上(z=0)任意给定不同的三点A,B,C,求上半球面上(z≥0)上的一点D,使得距离和|AD|+|BD|+|CD|取得最大值.通过数值搜索知道,使距离和取得最大值的点D很多情况下位于赤道上,少数情况下位于半球面内部.通过角度计算,同时借助计算机辅助推导,发现了点D在大多数情况下位于...     

Radial Solutions and Orthogonal Trajectories in Multiobjective Global Optimization

This paper presents a new, ray-oriented method for the global solution of nonscalarized vector optimization problems and a framework for the application of the Karush–Kuhn–Tucker theorem to such problems. Properties of nonlinear multiobjective problems implied by the Karush–Kuhn–Tucker necessary conditions are investigated. The regular case specific to nonscalarized MOPs is singled out when a nonlinear MOP with nonlinearities only in the constraints reduces to a nondegenerate linear system. It is shown that the trajectories of the Lagrange multipliers corresponding to the components of the vector cost function are orthogonal to the corresponding trajectories of the vector deviations in the balance space (to the balance set for Pareto solutions). Illustrative examples are presented.     

Pareto Analysis vis-à-vis Balance Space Approach in Multiobjective Global Optimization

There is much controversy about the balance space approach, introduced first in Ref. 1, pp. 138–140, with the consideration of the balance number and balance vectors, and then further developed in Ref. 2, with the consideration of balance points and balance sets. There were attempts to identify the balance space approach with some other methods of multiobjective optimization, notably the method proposed in Ref. 3 and most recently Pareto analysis, as presented in Ref. 4. In this paper, we compare Pareto analysis with the balance space approach on several examples to demonstrate the interrelation and the differences of the two methods. As a byproduct, it is shown that, in some cases, the entire Pareto sets, proper and adjoint, can be determined very simply, without any special investigation of the (nonscalarized, nonconvex) multiobjective global optimization problem. The method of parameter introduction is presented in application to determining the Pareto sets and balance set. The use of computer graphics software complemented with the Gauss–Jordan matrix reduction algorithm is proposed for a class of otherwise intractable problems with nonconvex constraint sets.     

Variable Programming: A Generalized Minimax Problem. Part I: Models and Theory

In this two-part series of papers, a new generalized minimax optimization model, termed variable programming (VP), is developed to solve dynamically a class of multi-objective optimization problems with non-decomposable structure. It is demonstrated that such type of problems is more general than existing optimization models. In this part, the VP model is proposed first, and the relationship between variable programming and the general constrained nonlinear programming is established. To illustrate its practicality, problems on investment and the low-side-lobe conformal antenna array pattern synthesis to which VP can be appropriately applied are discussed for substantiation. Then, theoretical underpinnings of the VP problems are established. Difficulties in dealing with the VP problems are discussed. With some mild assumptions, the necessary conditions for the unconstrained VP problems with arbitrary and specific activated feasible sets are derived respectively. The necessary conditions for the corresponding constrained VP problems with the mild hypotheses are also examined. Whilst discussion in this part is concentrated on the formulation of the VP model and its theoretical underpinnings, construction of solution algorithms is discussed in Part II.This work was supported by the RGC grant CUHK 152/96H of the Hong Kong Research Grant Council.     

广义函数多目标规划的充分条件

本文借助于广义函数的调和表示，采用非标准分析方法，给出广义函数多目标规划的几个充分条件．     

Equality Constraints in Multiobjective Robust Design Optimization: Decision Making Problem

Robust design optimization (RDO) problems can generally be formulated by incorporating uncertainty into the corresponding deterministic problems. In this context, a careful formulation of deterministic equality constraints into the robust domain is necessary to avoid infeasible designs under uncertain conditions. The challenge of formulating equality constraints is compounded in multiobjective RDO problems. Modeling the tradeoffs between the mean of the performance and the variation of the performance for each design objective in a multiobjective RDO problem is itself a complex task. A judicious formulation of equality constraints adds to this complexity because additional tradeoffs are introduced between constraint satisfaction under uncertainty and multiobjective performance. Equality constraints under uncertainty in multiobjective problems can therefore pose a complicated decision making problem. In this paper, we provide a new problem formulation that can be used as an effective multiobjective decision making tool, with emphasis on equality constraints. We present two numerical examples to illustrate our theoretical developments.     

Duality Theorems on Multi-objective Programming of Generalized Functions

The form of a dual problem of Mond-Weir type for multi-objective programming problems of generalized functions is defined and theorems of the weak duality, direct duality and inverse duality are proven.     

解非线性约束优化问题的依赖域算法

本文对一般非线性约束优化问题提出了一个信赖域算法,导出了等价的KKT条件.在试探步满足适当条件下,证明了算法的全局收敛性,并进行了数值试验.     

Finite Element Analysis of Free Material Optimization Problem

Free material optimization solves an important problem of structural engineering, i.e. to find the stiffest structure for given loads and boundary conditions. Its mathematical formulation leads to a saddle-point problem. It can be solved numerically by the finite element method. The convergence of the finite element method can be proved if the spaces involved satisfy suitable approximation assumptions. An example of a finite-element discretization is included.     

一个全局最优化问题的填充函数

本文给出了一个非线性全局最优化问题的填充函数定义,此定义不同于以前已有的填充函数定义。根据此定义,本文提出了一簇单参数填充函数和相应的填充函数算法．对几个算例的数据测试表明,该填充函数法是可行和有效的．