Journal of Global Optimization最新文献_第9页

The exact projective penalty method for constrained optimization 约束优化的精确投影惩罚法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-03 DOI: 10.1007/s10898-023-01350-4

{"title":"The exact projective penalty method for constrained optimization","authors":"","doi":"10.1007/s10898-023-01350-4","DOIUrl":"https://doi.org/10.1007/s10898-023-01350-4","url":null,"abstract":"<h3>Abstract</h3> <p>A new exact projective penalty method is proposed for the equivalent reduction of constrained optimization problems to nonsmooth unconstrained ones. In the method, the original objective function is extended to infeasible points by summing its value at the projection of an infeasible point on the feasible set with the distance to the projection. Beside Euclidean projections, also a pointed projection in the direction of some fixed internal feasible point can be used. The equivalence means that local and global minimums of the problems coincide. Nonconvex sets with multivalued Euclidean projections are admitted, and the objective function may be lower semicontinuous. The particular case of convex problems is included. The obtained unconstrained or box constrained problem is solved by a version of the branch and bound method combined with local optimization. In principle, any local optimizer can be used within the branch and bound scheme but in numerical experiments sequential quadratic programming method was successfully used. So the proposed exact penalty method does not assume the existence of the objective function outside the allowable area and does not require the selection of the penalty coefficient.</p>","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constrained multiobjective optimization of expensive black-box functions using a heuristic branch-and-bound approach 使用启发式分支和边界方法对昂贵的黑盒子函数进行有约束的多目标优化

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-02 DOI: 10.1007/s10898-023-01336-2

{"title":"Constrained multiobjective optimization of expensive black-box functions using a heuristic branch-and-bound approach","authors":"","doi":"10.1007/s10898-023-01336-2","DOIUrl":"https://doi.org/10.1007/s10898-023-01336-2","url":null,"abstract":"<h3>Abstract</h3> <p>While constrained, multiobjective optimization is generally very difficult, there is a special case in which such problems can be solved with a simple, elegant branch-and-bound algorithm. This special case is when the objective and constraint functions are Lipschitz continuous with known Lipschitz constants. Given these Lipschitz constants, one can compute lower bounds on the functions over subregions of the search space. This allows one to iteratively partition the search space into rectangles, deleting those rectangles which—based on the lower bounds—contain points that are all provably infeasible or provably dominated by previously sampled point(s). As the algorithm proceeds, the rectangles that have not been deleted provide a tight covering of the Pareto set in the input space. Unfortunately, for black-box optimization this elegant algorithm cannot be applied, as we would not know the Lipschitz constants. In this paper, we show how one can heuristically extend this branch-and-bound algorithm to the case when the problem functions are black-box using an approach similar to that used in the well-known DIRECT global optimization algorithm. We call the resulting method “simDIRECT.” Initial experience with simDIRECT on test problems suggests that it performs similar to, or better than, multiobjective evolutionary algorithms when solving problems with small numbers of variables (up to 12) and a limited number of runs (up to 600). </p>","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determining solution set of nonlinear inequalities using space-filling curves for finding working spaces of planar robots 利用空间填充曲线确定非线性不等式的解集，以寻找平面机器人的工作空间

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-02 DOI: 10.1007/s10898-023-01352-2

Daniela Lera, Maria Chiara Nasso, Mikhail Posypkin, Yaroslav D. Sergeyev

引用次数: 0

DC-programming for neural network optimizations 神经网络优化的直流编程

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2024-01-02 DOI: 10.1007/s10898-023-01344-2

引用次数: 0

Extragradient-type methods with $$mathcal {O}left( 1/kright) $$ last-iterate convergence rates for co-hypomonotone inclusions 具有$$mathcal {O}left( 1/kright) $$共hypomonotone 内含物最后迭代收敛率的外梯度型方法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-12-16 DOI: 10.1007/s10898-023-01347-z

Quoc Tran-Dinh

引用次数: 0

A Bregman inertial forward-reflected-backward method for nonconvex minimization 用于非凸最小化的布雷格曼惯性前向-反射-后向方法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-12-16 DOI: 10.1007/s10898-023-01348-y

Xianfu Wang, Ziyuan Wang

引用次数: 0

A surrogate-assisted evolutionary algorithm with clustering-based sampling for high-dimensional expensive blackbox optimization 基于聚类采样的代理辅助进化算法，用于高维昂贵的黑箱优化

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-12-14 DOI: 10.1007/s10898-023-01343-3

Fusheng Bai, Dongchi Zou, Yutao Wei

{"title":"A surrogate-assisted evolutionary algorithm with clustering-based sampling for high-dimensional expensive blackbox optimization","authors":"Fusheng Bai, Dongchi Zou, Yutao Wei","doi":"10.1007/s10898-023-01343-3","DOIUrl":"https://doi.org/10.1007/s10898-023-01343-3","url":null,"abstract":"<p>Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive function evaluations considerably limits the number of true objective function evaluations allowed in order to find a good solution. In this paper, we propose a clustering-based surrogate-assisted evolutionary algorithm, in which a clustering-based local search technique is embedded into the radial basis function surrogate-assisted evolutionary algorithm framework to obtain sample points which might be close to the local solutions of the actual optimization problem. The algorithm generates sample points cyclically by the clustering-based local search, which takes the cluster centers of the ultimate population obtained by the differential evolution iterations applied to the surrogate model in one cycle as new sample points, and these new sample points are added into the initial population for the differential evolution iterations of the next cycle. In this way the exploration and the exploitation are better balanced during the search process. To verify the effectiveness of the present algorithm, it is compared with four state-of-the-art surrogate-assisted evolutionary algorithms on 24 synthetic test problems and one application problem. Experimental results show that the present algorithm outperforms other algorithms on most synthetic test problems and the application problem.</p>","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138630136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global optimization of mixed-integer nonlinear programs with SCIP 8 利用 SCIP 对混合整数非线性程序进行全局优化 8

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-12-14 DOI: 10.1007/s10898-023-01345-1

Ksenia Bestuzheva, Antonia Chmiela, Benjamin Müller, Felipe Serrano, Stefan Vigerske, Fabian Wegscheider

引用次数: 0

Variable sample-size optimistic mirror descent algorithm for stochastic mixed variational inequalities 随机混合变分不等式的可变样本大小乐观镜像下降算法

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-12-11 DOI: 10.1007/s10898-023-01346-0

Zhen-Ping Yang, Yong Zhao, Gui-Hua Lin

引用次数: 0

On inexact versions of a quasi-equilibrium problem: a Cournot duopoly perspective 准均衡问题的不精确版本:古诺双寡头视角

IF 1.8 3区数学

Journal of Global Optimization Pub Date : 2023-11-24 DOI: 10.1007/s10898-023-01341-5

E. L. Dias Júnior, P. J. S. Santos, A. Soubeyran, J. C. O. Souza

引用次数: 0