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global optimization Method Using SLE and Adaptive RBF Based on Fuzzy Clustering

Chinese Journal of Mechanical Engineering 2012年第4期25卷 768-775页

作者： ZHU Huaguang LIU Li LONG Teng ZHAO Junfeng School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization ***,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually *** order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation *** efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is *** this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels *** method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually *** fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design *** numerical benchmark examples are used for validating the performance of *** results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design *** method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

关键词： global optimization Latin hypercube design radial basis function fuzzy clustering adaptive response surface method

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global optimization design method for maximizing the capacity of V-belt drive

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Science China(Technological Sciences) 2011年第1期54卷 140-147页

作者： ZHANG ShaoJun WAN Zhong LIUGuangLian School of Mathematics Sciences and Computing Technology Central South University Changsha 410083 China College of Mechanical and Electrical Engineering Central South University Changsha 410083 China

An optimization model is constructed to formulate the maximization problem on the capacity of V-belt drive. The concavity,the monotonicity and the global optimality condition are studied for the objective function,and it is proved that the feasible region of the model is bounded,closed and convex under some design conditions. Then,a solution method,called an optimal segment algorithm,is developed to find the global maximizer of the model. Under four different design conditions,solution methods are presented respectively. Some real case studies are employed to demonstrate that the model and the algorithm in this paper are promising.

关键词： V.belt global optimization capacity of drive optimal segment algorithm

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global optimization Method for Linear Multiplicative Programming

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Acta Mathematicae Applicatae Sinica 2015年第2期31卷 325-334页

作者： Xue-gang ZHOU Bing-yuan CAO Kun WU School of Mathematics and Information Science Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes Guangzhou University Department of Applied Mathematics Guangdong University of Finance School of Mathematical Science and Computing Technology Center South University

In this paper, a new global algorithm is presented to globally solve the linear multiplicative programming（LMP）. The problem（LMP） is firstly converted into an equivalent programming problem（LMP（H））by introducing p auxiliary variables. Then by exploiting structure of（LMP（H））, a linear relaxation programming（LP（H）） of（LMP（H）） is obtained with a problem（LMP） reduced to a sequence of linear programming problems. The algorithm is used to compute the lower bounds called the branch and bound search by solving linear relaxation programming problems（LP（H））. The proposed algorithm is proven that it is convergent to the global minimum through the solutions of a series of linear programming problems. Some examples are given to illustrate the feasibility of the proposed algorithm.

关键词： linear multiplicative programming global optimization linear programming branch and bound

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global optimization‑Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi‑objective optimization Algorithm

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Automotive Innovation 2023年第3期6卷 492-507页

作者： Kegang Zhao Kunyang He Zhihao Liang Maoyu Mai School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou510610China

The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy *** global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in the development of *** paper presents a multi-objective global optimization algorithm for the EMS of *** algorithm combines the Radau Pseudospectral Knotting Method(RPKM)and the Nondominated Sorting Genetic Algorithm(NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving *** driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using *** RPKM results serve as the fitness values in iteration through the NSGA-II *** results of the algorithm applied to a PHEV simulation show a 26.74%–53.87%improvement in both objectives after 20 iterations compared to the solutions obtained using only *** proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality,with the added benefits of faster and more uniform solutions.

关键词： Plug-in hybrid electric vehicles Energy management strategy Multi-objective optimization global optimization NSGA-II Radau pseudospectral knotting method

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global optimization OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

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Chinese Journal of Mechanical Engineering 2004年第2期17卷 247-252页

作者： ZhangGuijun WuTihua YeRong CollegeofElectronicandElectricEngineering ShanghaiJiaotongUniversityShanghai200030China HebeiAcademyofSciences Shijiazhuang050081China

An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.

关键词： Pump configuration problem Extended crowding genetic algorithm Speciesconserving Composite encoding global optimization

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global optimization over linear constraint non-convex programming problem

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Journal of Harbin Institute of Technology(New Series) 2005年第6期12卷 650-655页

作者：张贵军吴惕华叶蓉杨海清 College of Information Zhejiang University of Technology Hangzhou 310032 China Hebei Academy of Sciences Shijiazhuang 050081 China

A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.

关键词： global optimization linear constraint steady state genetic algorithms extremes encode convex crossover

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global optimization by small-world optimization algorithm based on social relationship network

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Journal of Central South University 2012年第8期19卷 2247-2265页

作者：李晋航邵新宇龙渊铭朱海平 B.R.Schlessman School of Mechanical Science and Engineering Huazhong University of Science and Technology Air Force Research Laboratory Wright Patterson Air Force Base

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

关键词： global optimization intelligent algorithm small-world optimization decentralized search

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global optimization Using Diffusion Perturbations with Large Noise Intensity

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Acta Mathematicae Applicatae Sinica 2006年第4期22卷 529-542页

作者： G. Yin K. Yin Department of Mathematics Wayne State University Detroit MI 48202 Department of Bio-based Products University of Minnesota Saint Paul MN 55108

This work develops an algorithm for global optimization. The algorithm is of gradient ascent type and uses random perturbations. In contrast to the annealing type procedures, the perturbation noise intensity is large. We demonstrate that by properly varying the noise intensity, approximations to the global maximum can be achieved. We also show that the expected time to reach the domain of attraction of the global maximum, which can be approximated by the solution of a boundary value problem, is finite. Discrete-time algorithms are proposed; recursive algorithms with occasional perturbations involving large noise intensity are developed. Numerical examples are provided for illustration.

关键词： global optimization random perturbation diffusion

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Wind Driven Butterfly optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for global optimization and PID Controller Design

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Journal of Bionic Engineering 2023年第6期20卷 2935-2972页

作者： Yang He Yongquan Zhou Yuanfei Wei Qifang Luo Wu Deng College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China College of Electronic Information and Automation Civil Aviation University of ChinaTianjin300300China Faculty of Information Science and Technology Universiti Kebangsaan Malaysia43600BangiSelangorMalaysia Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

This paper presents a Butterfly optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as *** further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind *** algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the ***,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching *** at improving the explorative performance at the initial stages and later stages,the fragrance generation method was *** evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark ***,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC ***,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter *** results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA.

关键词： Butterfly optimization Algorithm(BOA) Wind Driven optimization(WDO) Benchmark functions global optimization Proportional integral derivative(PID) Metaheuristic

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An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for global optimization Problems

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Journal of Bionic Engineering 2022年第4期19卷 1177-1202页

作者： Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran

The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is *** algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic *** paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization ***,we introduced a version of this algorithm called the QLGCTSA *** of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation *** algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering *** results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.

关键词： Tunicate swarm algorithm(TSA) Mutation strategy global optimization

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