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Improved Dwarf Mongoose optimization for constrained Engineering Design Problems

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

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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 ones.However,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 techniques.Although 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 model.This study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud environments.Based on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this problem.With 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 criteria.Experimental 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

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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

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An improved flexible tolerance method for solving nonlinear constrained optimization problems:Application in mass integration

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Chinese Journal of Chemical Engineering 2017年第5期25卷 617-631页

作者： Alice Medeiros Lima Wu Hong Kwong Antonio José Goncalves Cruz Chemical Engineering Graduate Program Federal University of Sao Carlos Department of Chemical Engineering Department of Chemical Engineering Federal University of Sao Carlos

This paper proposes the use of the flexible tolerance method(FTM) modified with adaptive Nelder–Mead parameters and barrier to solve constrained optimization problems. The problems used to analyze the performance of the methods were taken from G-Suite functions, and the methods with the best performance were applied in mass integration problems. Four methods were proposed:(1) flexible tolerance method(FTM) using adaptive parameters(FTMA),(2) flexible tolerance method with scaling(FTMS) and with adaptive parameters(FTMAS),(3) FTMS including the barrier modification(MFTMS) and(4) MFTMS hybridized with PSO(MFTMS-PSO). The success rates of these methods were 100%(MFTMS), 85%(MFTMS-PSO), 40%(FTMAS) and 30%(FTMA).Numerical experiments indicated that the MFTMS could efficiently and reliably improve the accuracy of global optima. In mass integration, the method was able, from current process situation, to reach the optimum process configuration that includes integration issues, which was not possible using FTM in its standard formulation. The hybridization of FTMS with PSO(without barrier), FTMS-PSO, was also able to solve mass integration problems efficiently.

关键词： Flexible tolerance method Adaptive parameters Scaling constrained optimization Barrier PSO Mass integration

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A Filter Line Search Algorithm Based on an Inexact Newton Method for Nonconvex Equality constrained optimization

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 687-698页

作者： Zhu-jun WANG De-tong ZHU Cun-yun NIE College of Science Hunan Institute of Engineering College of Mathematics and Science Shanghai Normal University

We propose an inexact Newton method with a filter line search algorithm for nonconvex equality constrained optimization. Inexact Newton＇s methods are needed for large-scale applications which the iteration matrix cannot be explicitly formed or factored. We incorporate inexact Newton strategies in filter line search, yielding algorithm that can ensure global convergence. An analysis of the global behavior of the algorithm and numerical results on a collection of test problems are presented.

关键词： nonconvex constrained optimization filter line search inexact Newton method

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A Subspace Version of the Powell–Yuan Trust-Region Algorithm for Equality constrained optimization

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Journal of the Operations Research Society of China 2013年第4期1卷 425-451页

作者： Geovani Nunes Grapiglia Jinyun Yuan Ya-xiang Yuan Departamento de Matemática Universidade Federal do ParanáCentro PolitécnicoCx.postal 19.08181531-980CuritibaParanáBrazil The Capes Foundation Ministry of Education of BrazilCx.postal 25070.040-020BrasíliaDistrito FederalBrazil State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesZhongguancundonglu 55Beijing 100190P.R.China

This paper studied subspace properties of the Celis–Dennis–Tapia(CDT)subproblem that arises in some trust-region algorithms for equality constrained optimization.The analysis is an extension of that presented by Wang and Yuan(Numer.Math.104:241–269,2006)for the standard trust-region subproblem.Under suitable conditions,it is shown that the trial step obtained from the CDT subproblem is in the subspace spanned by all the gradient vectors of the objective function and of the constraints computed until the current iteration.Based on this observation,a subspace version of the Powell–Yuan trust-region algorithm is proposed for equality constrained optimization problems where the number of constraints is much lower than the number of variables. The convergence analysis is given and numerical results arealso reported.

关键词： constrained optimization Trust-region methods Subspace methods

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A relative feasibility degree based approach for constrained optimization problems

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第4期11卷 249-260页

作者： Cheng-gang CUI Yan-jun LI Tie-jun WU Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China

Based on the ratio of the size of the feasible region of constraints to the size of the feasible region of a constrained optimization problem,we propose a new constraint handling approach to improve the efficiency of heuristic search methods in solving the constrained optimization problems.In the traditional classification of a solution candidate,it is either a feasible or an infeasible solution.To refine this classification,a new concept about the relative feasibility degree of a solution candidate is proposed to represent the amount by which the 'feasibility' of the solution candidate exceeds that of another candidate.Relative feasibility degree based selection rules are also proposed to enable evolutionary computation techniques to accelerate the search process of reaching a feasible region.In addition,a relative feasibility degree based differential evolution algorithm is derived to solve constraint optimization problems.The proposed approach is tested with nine benchmark problems.Results indicate that our approach is very competitive compared with four existing state-of-the-art techniques,though still sensitive to the intervals of control parameters of the differential evolution.

关键词： constrained optimization Evolutionary computation Relative feasibility degree(RFD) Evolution differential algorithm

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A Quadratically Approximate Framework for constrained optimization,Global and Local Convergence

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Acta Mathematica Sinica,English Series 2008年第5期24卷 771-788页

作者： Jin Bao JIAN College of Mathematics and Information Science Guangxi University Nanning 530004 P. R. China

This paper presents a quadratically approximate algorithm framework （QAAF） for solving general constrained optimization problems, which solves, at each iteration, a subproblem with quadratic objective function and quadratic equality together with inequality constraints. The global convergence of the algorithm framework is presented under the Mangasarian-Fromovitz constraint qualification （MFCQ）, and the conditions for superlinear and quadratic convergence of the algorithm framework are given under the MFCQ, the constant rank constraint qualification （CRCQ） as well as the strong second-order sufficiency conditions （SSOSC）. As an incidental result, the definition of an approximate KKT point is brought forward, and the global convergence of a sequence of approximate KKT points is analysed.

关键词： constrained optimization quadratic approximation algorithm framework quadratic constraints global and local convergence

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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 iterations.The 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.

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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.

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