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Science China Mathematics 2014年第6期57卷 1287-1302页

作者： XUE Dan SUN WenYu School of Mathematical Sciences Jiangsu Key Labratory for NSLSCSNanjing Normal University

In this paper,we propose a derivative-free trust region algorithm for constrained minimization problems with separable structure,where derivatives of the objective function are not available and cannot be directly *** each iteration,we construct a quadratic interpolation model of the objective function around the current *** new iterates are generated by minimizing the augmented Lagrangian function of this model over the trust *** filter technique is used to ensure the feasibility and optimality of the iterative *** convergence of the proposed algorithm is proved under some suitable assumptions.

关键词： constrained optimization derivative-free optimization multivariate interpolation separable op-timization global convergence

A SUPERLINEARLY AND QUADRATICALLY CONVERGENT SQP TYPE FEASIBLE METHOD FOR constrained optimization

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Applied Mathematics(A Journal of Chinese Universities) 2000年第3期15卷 319-331页

作者： JianJinbao ZhangKecun XueShengjia Dept.ofMath. GuangxiUniv.Nanning530004 SchoolofScience Xi＇anJiaotongUniv.Xi＇an710049 AdministrationInstituteofJinanUniv. Guangzhou510632

A new SQP type feasible method for inequality constrained optimization is presented,it is a combination of a master algorithm and an auxiliary algorithm which is taken only in finite *** directions of the master algorithm are generated by only one quadratic programming, and its step\|size is always one, the directions of the auxiliary algorithm are new “second\|order” feasible descent. Under suitable assumptions,the algorithm is proved to possess global and strong convergence, superlinear and quadratic convergence.

关键词： constrained optimization SQP feasible method,convergence rate of convergence.

A TRUST REGION METHOD WITH A CONIC MODEL FOR NONLINEARLY constrained optimization

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Applied Mathematics(A Journal of Chinese Universities) 2006年第3期21卷 263-275页

作者： Wang Chengjing Dept.of Math. Zhejiang Univ. Hangzhou 310028 China.

Trust region methods are powerful and effective optimization methods. The conic model method is a new type of method with more information available at each iteration than standard quadratic-based methods. The advantages of the above two methods can be combined to form a more powerful method for constrained optimization. The trust region subproblem of our method is to minimize a conic function subject to the linearized constraints and trust region bound. At the same time, the new algorithm still possesses robust global properties. The global convergence of the new algorithm under standard conditions is established.

关键词： trust region method conic model constrained optimization nonlinear programming.

A New Unified Path to Smoothing Nonsmooth Exact Penalty Function for the constrained optimization

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Open Journal of optimization 2021年第3期10卷 61-70页

作者： Bingzhuang Liu School of Mathematics and Statistics Shandong University of Technology Zibo China

We propose a new unified path to approximately smoothing the nonsmooth exact penalty function in this paper. Based on the new smooth penalty function, we give a penalty algorithm to solve the constrained optimization ... 详细信息

关键词： Penalty Function constrained optimization Smoothing Method Optimal Solution

A TRUST REGION ALGORITHM FOR constrained NONSMOOTH optimization PROBLEMS

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Journal of Computational Mathematics 2001年第4期19卷 357-364页

作者： Yu-fei Yang Dong-hui Li Hunan Univ Inst Appl Math Changsha 410082 Peoples R China

Presents an inexact trust region algorithm for solving constrained nonsmooth optimization problems. Global convergence of the algorithm; Assumptions on the algorithm; Relation between critical points and stationary po... 详细信息

关键词： trust region method nonsmooth function constrained optimization global convergence

A Modified Algorithm for Multi-objective constrained optimization Problems

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A Modified Algorithm for Multi-objective Constrained Optimiz...

A Level-Value Estimate Method for Solving constrained Global optimization

2011 Chinese Control and Decision Conference（CCDC）

作者： Lin Feng is with Northeastern University,Shenyang,CHINA .Zhizhong Mao is with Northeastern University,Shenyang,CHINA .Ping Yuan is with Northeastern University,Shenyang,CHINA

A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory summation function of constraint conditions,to accelerate the convergence rate,a new region changed acceleration mechanism is used,and for shake of improving the local search ability,chaos search technology is *** modified algorithm not only improves the diversity of solution set but also makes the nondominated solutions approach the Pareto set as close as *** last,the algorithm is applied to three classical test functions;the optimization performance of modified algorithm is evaluated and numerical experimental results show the effectiveness of the proposed method.

关键词： Particle swarm optimization algorithm Multiobjective optimization constrained optimization Region changed acceleration operator Chaotic operator.

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Journal of Shanghai University(English Edition) 2004年第2期8卷 128-131页

作者：俞武扬邬冬华吕瑜佩 Department of Mathematics

The penalty method is a popular method for solving constrained optimization problems, which can change the constrained optimization to the unconstrained optimization. With the integral-level set method, a new approach was proposed, which is briefer than the penalty method, to achieve the transform by constructing a simple function, then a level-value function was introduced to construct the equivalence between the unconstrained optimization and a nonlinear equality. By studying the properties of the function, a level-value estimate algorithm and an implementation algorithm were given by means of the uniform distribution of the good point set. Key words global optimization - constrained optimization - integral-level set - level-value estimate MSC 2000 90C05

关键词： global optimization constrained optimization integral-level set level-value estimate

Improved Dwarf Mongoose optimization for constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms constrained optimization Unconstrained optimization Engineering design problems

Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems

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Journal of Central South University 2015年第6期22卷 2250-2259页

作者：蔡绍洪龙文焦建军 Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance & Economics School of Mathematics and Statistics Guizhou University of Finance & Economics

A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony(ABC) algorithm with biogeography-based optimization(BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm's performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches.

关键词： artificial bee colony biogeography-based optimization constrained optimization mechanical design problem

Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua Science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling