一种修正的求约束总极值的积分-水平集方法

对于有约束的全局最优化问题,在Chew-Zheng的《Integral Global Optimization》和邬冬华等的《一种修正的求总极值的积分-水平集方法的实现算法收敛性》的基础上,给出一种修正的求约束总极值的积分-水平集方法,它同样具有修正的求总极值的积分-水平集方法的两个特点: 1) 每一步构造一个新函数,它与原目标函数具有相同的总极值; 2) 避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平集的困难．同时给出了其实现算法,并证明了算法的收敛性．     

求解多目标问题的积分型算法的最优性条件

本文把修正的积分水平集法与多目标分层序列法相结合,在文[9]给出的一 种求解多目标最优化的积分型实现算法的基础上提出了相关均值与相关方差的概念,并证 明了与相关均值和相关方差有关的多目标全局有效解存在的充要条件和全局弱有效解存 在的充分条件,即最优性条件．     

解非线性经补问题的积分水平集方法

本文考虑有约束的非线性互补问题的全局最优化问题,在文《Integral Global Optimization Method fro Solution of Nonlinear Complementarity problem》和《一的求总极值的积分－水平集方法》的基础上,给出了一种修正的求约束总极值的积分－水平集方法,它同样具有修正的求总极值的积分－水平集方法的两个特点：1）第一步需要构造一个新的函数,而且它与原目标函数具有相同的总极值;2）避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平的困难,并证明了算法的收敛性。     

解非线性互补问题的积分水平集方法

本文考虑有约束的非线性互补问题的全局最优化问题,在文《IntegralGlobalOptimizationMethodforSolutionofNonlinearComplementarityproblem》和《一种修正的求总极值的积分一水平集方法》的基础上,给出了一种修正的求约束总极值的积分一水平集方法,它同样具有修正的求总极值的积分一水平集方法的两个特点：1）每一步需要构造一个新的函数,而且它与原目标函数具有相同的总极值;2）避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平的困难,并证明了算法的收敛性     

变测度的积分型全局优化算法

郑权等首先提出积分-水平集求总极值的方法,实现算法中采用Monte-Carlo 随机投点产生近似水平集来缩小搜索区域范围,但这一算法可能失去总极值点．此后,邬 冬华等给出了一种修正的积分－水平集的方法,一种区域不收缩的分箱方法以保证总极 值点不被丢失．本文在此基础上采取对不同的箱子采用不同的测度这一策略,使水平值 更充分的下降,更快的达到全局极小值,以提高修正算法的计算效率．最后给出的数值算 例说明了算法是有效的．     

积分型总极值方法的最优性条件

郑权提出了求总极值问题的积分—水平集的概念性算法,同时给出了最优性条件.本文构造函数F(x),讨论了该函数的性质,证明求解原问题等价于求解方程F(c)=0的根.在文中给出了相应的总极值存在的最优性条件.     

积分—水平集总极值算法的另一实现途径

在（１）中提出了一个积分－水平集求总极值的概念性算法及Ｍｏｎｔｅ－Ｃａｒｌｏ随机投点的实现途径，并在不少实际问题中得到了很好的应用。但这一实际算法的收敛性是个未解决的问题。本文给出了另一实现途径，并证明了收敛性。从而从理论上证明了这一实现算法一定能求到总极值和总极值点，数值试验结果也支持这一理论结果。     

求总极值问题的最优性条件

郑权提出了求总极值问题的积分－水平集的概念性算法,同时给出了最优性条件。本文提出了修正的积分－水平集算法,并且给出了类似的总极值存在的最优性条件。     

一种修正的求总极值的积分—水平集方法的实现算法收敛性

1978年,郑权等提出了一个积分型求总极值的概念性算法及Monte-Carlo随机投点的实现算法,给出了概念性算法的总极值存在的充分必要条件,但是其实现算法收敛性仍未解决,1986年,张连生等给出离散均值－水平集的实现算法,并证明了它的收敛性。本文给出修正的积分－水平集方法,用一致分布搂九值积分逼近水平集构造实现算法,并证明了算法的收敛性。     

一个求总极值的实现算法及其收敛性

1978年,郑权等首先提出了一种用积分─水平集求总极值的方法及用Monte－Carlo随机投点实现的实现其法,其实现算法是否收敛未解决的问题．本文提出一种用数论方法实现的实现算法,并证明了该实现其法是收敛的．初步的数值结果表明,该实现其法是较有效的．     

一种改进的GDF多目标优化算法

本文提出一种交互式非线性多目标优化算法，该算法是ＧＤＦ多目标优化算法的改进，具有这样的特点：算法采用了既约设计空间策略，具有良好的收敛性；算法生成的迭代点是有效解；算法具有多种一维搜索准则；对于线性多目标问题，算法只需一次交互迭代即可示出多目标问题的最优解。     

A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization

Recently, a general-purpose local-search heuristic method called extremal optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in numerical multiobjective optimization and proposes a new novel elitist (1 + λ) multiobjective algorithm, called multiobjective extremal optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems, the Pareto dominance strategy is introduced to the fitness assignment of the proposed approach. We also present a new hybrid mutation operator that enhances the exploratory capabilities of our algorithm. The proposed approach is validated using five popular benchmark functions. The simulation results indicate that the proposed approach is highly competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOEO can be considered a good alternative to solve numerical multiobjective optimization problems.     

Multicriteria optimization with a multiobjective golden section line search

This work presents an algorithm for multiobjective optimization that is structured as: (i) a descent direction is calculated, within the cone of descent and feasible directions, and (ii) a multiobjective line search is conducted over such direction, with a new multiobjective golden section segment partitioning scheme that directly finds line-constrained efficient points that dominate the current one. This multiobjective line search procedure exploits the structure of the line-constrained efficient set, presenting a faster compression rate of the search segment than single-objective golden section line search. The proposed multiobjective optimization algorithm converges to points that satisfy the Kuhn-Tucker first-order necessary conditions for efficiency (the Pareto-critical points). Numerical results on two antenna design problems support the conclusion that the proposed method can solve robustly difficult nonlinear multiobjective problems defined in terms of computationally expensive black-box objective functions.     

Multiobjective Search Algorithm with Subdivision Technique

This paper presents a multiobjective search algorithm with subdivision technique (MOSAST) for the global solution of multiobjective constrained optimization problems with possibly noncontinuous objective or constraint functions. This method is based on a random search method and a new version of the Graef-Younes algorithm and it uses a subdivision technique. Numerical results are given for bicriterial test problems.     

Generalized Homotopy Approach to Multiobjective Optimization

This paper proposes a new generalized homotopy algorithm for the solution of multiobjective optimization problems with equality constraints. We consider the set of Pareto candidates as a differentiable manifold and construct a local chart which is fitted to the local geometry of this Pareto manifold. New Pareto candidates are generated by evaluating the local chart numerically. The method is capable of solving multiobjective optimization problems with an arbitrary number k of objectives, makes it possible to generate all types of Pareto optimal solutions, and is able to produce a homogeneous discretization of the Pareto set. The paper gives a necessary and sufficient condition for the set of Pareto candidates to form a (k-1)-dimensional differentiable manifold, provides the numerical details of the proposed algorithm, and applies the method to two multiobjective sample problems.     

求多目标优化问题Pareto最优解集的方法

主要讨论了无约束多目标优化问题Pareto最优解集的求解方法,其中问题的目标函数是C1连续函数.给出了Pareto最优解集的一个充要条件,定义了α强有效解,并结合区间分析的方法,建立了求解无约束多目标优化问题Pareto最优解集的区间算法,理论分析和数值结果均表明该算法是可靠和有效的.     

An interval entropy method for equality constrained multiobjective optimization problems

Based on the maximum entropy principle and the idea of a penalty function, an evaluation function is derived to solve multiobjective optimization problems with equality constraints. Combining with interval analysis method, we define a generalized Krawczyk operator, design interval iteration with constrained functions and new region deletion test rules, present an interval algorithm for equality constrained multiobjective optimization problems, and also prove relevant properties. A theoretical analysis and numerical results indicate that the algorithm constructed is effective and reliable.     

An Efficient Sampling Approach to Multiobjective Optimization

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savings of this new algorithm versus the traditional constraint method increase dramatically when the number of objectives increases. A real world case study of multiobjective optimal design of a best available control technology for Nitrogen Oxides (NOx) and Sulfur Oxides (SOx) reduction illustrates the usefulness of this approach.     

Multiple reduced gradient method for multiobjective optimization problems

Multiobjective optimization has a large number of real-life applications. Under this motivation, in this paper, we present a new method for solving multiobjective optimization problems with both linear constraints and bound constraints on the variables. This method extends, to the multiobjective setting, the classical reduced gradient method for scalar-valued optimization. The proposed algorithm generates a feasible descent direction by solving an appropriate quadratic subproblem, without the use of any scalarization approaches. We prove that the sequence generated by the algorithm converges to Pareto-critical points of the problem. We also present some numerical results to show the efficiency of the proposed method.     

Convergence analysis of Tikhonov-type regularization algorithms for multiobjective optimization problems

In this paper, we introduce a vector-valued Tikhonov-type regularization algorithm for an extended-valued multiobjective optimization problem. Under some mild conditions, we prove that any sequence generated by this algorithm converges to a weak Pareto optimal solution of the multiobjective optimization problem. Our results improve and generalize some known results.