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An Efficient differential evolution for Truss Sizing Optimization Using AdaBoost Classifier

Computer Modeling in Engineering & Sciences 2023年第1期134卷 429-458页

作者： Tran-Hieu Nguyen Anh-Tuan Vu Hanoi University of Civil Engineering Hanoi100000Vietnam

Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses thatmust be *** a surrogatemodel to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this ***,most existing surrogate models have been designed based on regression *** paper proposes a novel method,called CaDE,which adopts a machine learning classification technique for enhancing the performance of the differential evolution(DE)*** proposed method is separated into two *** the first optimization stage,the original DE is implemented as usual,but all individuals produced in this phase are stored as inputs of the training *** on design constraints verification,these individuals are labeled as“safe”or“unsafe”and their labels are saved as outputs of the training *** collecting enough data,an AdaBoost model is trained to evaluate the safety state of *** model is then used in the second stage to preliminarily assess new individuals,and unpromising ones are rejected without checking design *** method reduces unnecessary structural analyses,thereby shortens the optimization *** benchmark truss sizing optimization problems are solved using the proposed method to demonstrate its *** obtained results show that the CaDE finds good optimal designs with less structural analyses in comparison with the original DE and four other DE *** reduction rate of five examples ranges from 18 to over 50%.Moreover,the proposed method is applied to a real-size transmission tower design problem to exhibit its applicability in practice.

关键词： Structural optimization machine learning surrogate model differential evolution AdaBoost classifier

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A regeneratable dynamic differential evolution algorithm for neural networks with integer weights

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第12期11卷 939-947页

作者： Jian BAO Yu CHEN Jin-shou YU School of lnformation Science and Engineering East China Universi~ of Science and Technology Shanghai 200237 China Institute of Sofrtware and Intelligent Technology Hangzhou Dianzi University Hangzhou 310018 China

Neural networks with integer weights are more suited for embedded systems and hardware implementations than those with real weights. However, many learning algorithms, which have been proposed for training neural networks with float weights, are inefficient and difficult to train for neural networks with integer weights. In this paper, a novel regeneratable dynamic differential evolution algorithm (RDDE) is presented. This algorithm is efficient for training networks with integer weights. In comparison with the conventional differential evolution algorithm (DE), RDDE has introduced three new strategies: (1) A regeneratable strategy is introduced to ensure further evolution, when all the individuals are the same after several iterations such that they cannot evolve further. In other words, there is an escape from the local minima. (2) A dynamic strategy is designed to speed up convergence and simplify the algorithm by updating its population dynamically. (3) A local greedy strategy is introduced to improve local searching ability when the population approaches the global optimal solution. In comparison with other gradient based algorithms, RDDE does not need the gradient information, which has been the main obstacle for training networks with integer weights. The experiment results show that RDDE can train integer-weight networks more efficiently.

关键词： differential evolution Integer weights Neural networks Greedy Embedded systems Function approximation

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Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

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International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： Dieu Tien Bui Binh Thai Pham Quoc Phi Nguyen Nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam

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Gene sorting in differential evolution withcross-generation mutation

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Science China(Information Sciences) 2011年第2期54卷 268-278页

作者： TASSING Remi Department of Electronics and Information Engineering Huazhong University of Science and Technology

Gene sorting is proposed in this paper as a method of ordering trial vector’s component in dif- ferential evolution (DE). This method tends to significantly increase the convergence speed of DE with just a little modification on the original algorithm. In the meantime, a new concept of cross-generation mutation is introduced in order to perform the evolution process serially rather than parallelly. When combined with gene sorting, this method will further increase the convergence speed. A benchmark set of 18 functions is used to investigate the performance of these algorithms. Most importantly, the proposed methods can be incorporated in other variants of DE to further increase their respective speeds. Three versions of self-adaptive DE, namely iterated function system based adaptive differential evolution (IFDE), Janez’s DE (jDE) and SaDE, are taken as examples, which are averagely 10, 6 and 4 times faster than the benchmark set respectively.

关键词： differential evolution evolutionary algorithm genetic algorithm gene sorting cross-generation mutation

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Design of mixed H-two/H-infinity optimal control systems using multiobjective differential evolution algorithm

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控制理论与应用（英文版） 2013年第3期11卷 521-528页

作者： Lianghong WU Yaonan WANG Shaowu ZHOU Xiaofang YUAN Engineering Research Center of Advanced Mining Equipment Ministry of Education Hunan University of Science and Technology College of Electric and Information Engineering Hunan University

In this paper, the mixed H-two/H-infinity control synthesis problem is stated as a multiobjective opti-mization problem, with objectives of minimizing the H-two and H-infinity norms simultaneously. Instead of building a LMIs-based synthesis algorithm, a self-adaptive control parameter multiobjective differential evolution algorithm is developed directly in the controller parameters space. In the case of systems with polytopic uncertainties, the worst case norm computation is formulated as an implicit optimization problem, and the proposed self-adaptive differential evolution is employed to calculate the worst case H-two and H-infinity norms. The numerical examples illustrate the power and validity of the proposed approach for the mixed H-two/H-infinity control multiobjective optimal design.

关键词： Mixed H-two/H-infinity control Polytopic uncertainties Parameter self-adaptive differential evolution Multiobjective optimization

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Hybridization of differential evolution and Adaptive-Network-Based Fuzzy Inference Systemin Estimation of Compression Coefficient of Plastic Clay Soil

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha NguyenHai Nadhir Al-Ansari MahdisAmiri Hai-Bang Ly Indra Prakash Binh Thai Pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used differential evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam

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Cloud Data Center Selection Using a Modified differential evolution

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Computers, Materials & Continua 2021年第12期69卷 3179-3204页

作者： Yousef Sanjalawe Mohammed Anbar Salam Al-E’mari Rosni Abdullah Iznan Hasbullah Mohammed Aladaileh National Advanced IPv6 Center(NAv6) Universiti Sains Malaysia11800 USMPenangMalaysia Computer Science Department PY CollageNorthern Border University(NBU)9280 NBUAr’arKingdom of Saudi Arabia

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing *** data center selection challenges are compounded by ever-increasing users’requests and the number of data centers required to execute these *** service broker policy defines cloud data center’s selection,which is a case of an NP-hard problem that needs a precise solution for an efficient and superior *** evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness,and it is well suited for selecting an appropriate cloud data *** paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing *** differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data *** proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst *** results are compared with the state-of-arts cloud service broker policies.

关键词： Cloud computing data center data center selection cloud service broker differential evolution user request

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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on differential evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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Convergence Track Based Adaptive differential evolution Algorithm(CTbADE)

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Computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： differential evolution function optimization convergence track parameter sequence adaptive control parameters

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PID Neural Net work Decoupling Control Based on Hybrid Particle Swarm Optimization and differential evolution

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International Journal of Automation and computing 2020年第6期17卷 867-872页

作者： Hong-Tao Ye Zhen-Qiang Li School of Electrical and Information Engineering Guangxi University of Science and TechnologyLiuzhou 545006China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and TechnologyLiuzhou 545006China

For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between *** connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning *** order to solve this problem,a hybrid particle swarm optimization(PSO)and differential evolution(DE)algorithm(PSO-DE)is proposed for optimizing the connection weights of the *** DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several *** multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed *** results show t hat the proposed met hod has better decoupling capabilities and control quality than the previous approaches.

关键词： Particle swarm optimization differential evolution proportion integration differentiation(PID)neural network hybrid approach decoupling control.

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