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Simulation and Comparison Between Fuzzy Harmonic Search and differential evolution Algorithm: Type-2 Fuzzy Approach

IFAC-PapersOnLine 2022年第16期55卷 412-417页

作者： Himanshukumar R. Patel Vipul A. Shah Dharmsinh Desai University Nadiad-387001 Gujarat India

The harmony search (HS) and differential evolution (DE) algorithms are compared in this study. Additionally, an interval type-2 fuzzy logic system (IT2FLS) allowing dynamic change of the key parameters is offered for each algorithm. The use of fuzzy systems to dynamically alter the primary parameters for each algorithm seeks to improve the performance of the associated algorithms. The optimal design of fuzzy systems for benchmark control issues, particularly in fuzzy controller design, is used to evaluate and compare each algorithm (IT2FHS and IT2FDE). Simulation results demonstrate that the FHS method outperforms the FDE approach when it comes to fuzzy controller optimization. The better errors are found with the application of fuzzy systems to enhance each proposed algorithm, according to statistics.

关键词： Harmonic Search algorithm differential evolution algorithms Fuzzy controller Type-2 fuzzy systems Perturbation

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Automatic Column Grouping of 3D Steel Frames via Multi-Objective Structural Optimization

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BUILDINGS 2024年第1期14卷 191-191页

作者： Resende, Claudio Martha, Luiz Fernando Lemonge, Afonso Hallak, Patricia Carvalho, Jose Motta, Julia Pontifical Catholic Univ Rio de Janeiro Postgrad Program Civil & Environm Engn BR-22451900 Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro Dept Civil & Environm Engn BR-22451900 Rio De Janeiro Brazil Univ Fed Juiz de Fora Sch Engn Dept Appl & Computat Mech BR-36036900 Juiz De Fora Brazil Univ Fed Rio de Janeiro Civil Engn Program Coordinat Postgrad Programs Engn COPPE BR-21941909 Rio De Janeiro Brazil Univ Fed Juiz de Fora Civil Engn Program BR-36036900 Juiz De Fora Brazil

Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure's weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding.

关键词： automatic member grouping multi-objective optimization steel frames differential evolution algorithms

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Morphing Parametric Modeling and Design Optimization of Spoke and V-Type Permanent Magnet Machines by Combined Design of Experiments and differential evolution algorithms

Morphing Parametric Modeling and Design Optimization of Spok...

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IEEE Energy Conversion Congress and Exposition

作者： Peng Zhang Dan M. Ionel Nabeel A.O. Demerdash Marquette University Milwaukee WI Regal Beloit Corp. Grafton WI

ISBN: (纸本)9781479903375

In this paper, two novel morphing parametric models for flat-to-V-type (FV) and for spoke-to-V-type (SV) Permanent Magnet (PM) synchronous machine configurations are introduced. A combined multi-objective design optimization method employing Design of Experiments (DOE) and differential evolution (DE) algorithms was developed and implemented. A Central Composite Design (CCD) approach combined with the Response Surface Method (RSM) was used to perform a sensitivity study from which the significant independent design variables were selected for the DE design optimization procedure. A computationally efficient-Finite Element Analysis (CE-FEA) technique was employed to evaluate the electromagnetic performance of the candidate designs. Three optimization objectives are concurrently considered for minimum material cost, power losses and torque ripple, respectively. The DE optimization results enable the systematic comparison of three interior permanent magnet (IPM) motor topologies: flat-type, generic V-shape and spoke, respectively. A discussion on the relative merits of each topology is included.

关键词： spoke-type permanent magnet machine interior permanent magnet machines design of experiments finite element analysis response surface methodology design optimization differential evolution Optimal design Experimental Design interior permanent magnet machine response surface methodology Finite element method evolutionary algorithm differential evolution algorithms image morphing Permanent magnet synchronous machines electromagnetic properties MT

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Optimization of reinforcement layout of scissor-type bridge using differential evolution algorithm

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2019年第6期34卷 523-538页

作者： Chikahiro, Yuki Ario, Ichiro Pawlowski, Piotr Graczykowski, Cezary Holnicki-Szulc, Jan Shinshu Univ Dept Water Environm & Civil Engn Matsumoto Nagano Japan Hiroshima Univ Dept Civil & Environm Engn Hiroshima Japan Polish Acad Sci Inst Fundamental Technol Res Warsaw Poland

Scissors mechanisms are commonly used in safety engineering during the construction of temporary structures, owing to their inherent advantages of foldability, transformability, and reusability. We effectively utilized these scissors mechanism features to develop a lightweight, deployable emergency Mobile Bridge (MB) based on optimization, and control of the folding structure. Here, we discuss the problems of optimal reinforcement layout for the MB by formulating and solving three optimization problems, namely: (a) the load capacity maximization problem, (b) the weight minimization problem, and (c) coupling the load capacity maximization problem and the weight minimization problem. The potential benefits resulting from the application of reinforcement were evaluated using a combination of finite element analysis and an optimization algorithm based on the differential evolution method. The results demonstrate the significant positive influence of the additional reinforcing members. In particular, the limit load was increased by over 10 times, while the weight was decreased to approximately half. The proposed methodology enabled the development of a substantially improved version of the MB characterized by a higher load capacity and lower weight in comparison to the initial bridge design.

关键词： ultimate loads Optimization algorithms loading capacity Reusability distribution reinforcement Temporary structures scissor differential evolution algorithms MACHINE BASIN Bridge design Safety engineering evolutionary algorithm

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Neural network optimization and high-speed railway wheel-set size prediction forecasting based on differential evolution 11

Neural network optimization and high-speed railway wheel-set...

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11th International Conference on Information Optics and Photonics (CIOP)

作者： Zhang, Jiawen Zhang, Yu Luo, Lin Gao, Xiaorong Ling, Zhi Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 610031 Peoples R China

ISBN: (数字)9781510631748

ISBN: (纸本)9781510631748

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. In order to grasp the size information of High-speed railway wheel-set size in time and ensure the stable operation of high-speed railway, the size data of wheel-set are obtained by optical intercept image detection, and the LMBP neural network prediction model based on differential evolution is designed and implemented. The differential evolution algorithm (DE) is used to optimize the initial connection weights and thresholds between the layers of the neural network, and solve the problem that the back propagation (BP) network is easy to fall into the local extreme value due to the random initial connection weight and threshold. The Levenberg-Marquardt (LM) numerical algorithm is used to optimize the weights and thresholds in the BP network training process to solve the problem of long BP training time. According to the wheel diameter data of the CRH380 model, the effectiveness and accuracy of the method are verified by comparing the prediction results of different algorithms. Compared with the LMBP network and the standard BP network prediction model, the experimental results show that the DE-LMBP neural network model can obtain better correlation coefficients (0.9974), mean square error (0.0103), mean absolute error (0.0772) and average absolute percentage error (0.0084), which proves that the model is effective in predicting the size of the moving wheel and significantly improves the prediction accuracy.

关键词： differential evolution algorithms prediction model neural network Wheel-set size Levenberg-Marquardt algorithm

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An improved differential evolution Algorithm with Self Adaptive Mutation Strategies for Global Optimization

An improved Differential Evolution Algorithm with Self Adapt...

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Contemporary Computing and Applications (IC3A), International Conference on

作者： Sunil Kumar Gouda Ashok Kumar Mehta Department of Computer Science and Engineering Chaibasa Engineering College Chaibasa India Department of Computer Applications National Institute of Technology Jamshedpur India

ISBN: (数字)9781728154329

ISBN: (纸本)9781728154336

To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effective control parameters directly affect the performance and convergent rate of differential evolution method. Although its performance is very good but suffers from population diversity and *** this paper, new self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/1/bin is proposed. Elite archive strategies with dynamic adjustment of control parameter with booster vector added to afford more bandwidth for electing an effective mutant solution. The proposed algorithm is compared with five DE and six non-DE algorithms by using a set of twenty benchmark functions on COCO (comparing Continuous Optimizers) framework. The experimental result verifies that proposed self-adaptive strategies outperformed the competitors.

关键词： Control parameter differential evolution self-adaptive optimization control parameter self-adaptive Global optimization evolutionary algorithm differential evolution algorithms algorithms

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Inverse-designed terahertz modulators based on semiconductor multilayers

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SEMICONDUCTOR SCIENCE AND TECHNOLOGY 2021年第8期36卷

作者： Centeno, E. Alvear-Cabezon, E. Smaali, R. Moreau, A. Taliercio, T. Univ Clermont Auvergne CNRS SIGMA Clermont Inst Pascal F-63000 Clermont Ferrand France Univ Montpellier IES UMR CNRS 5214 Montpellier France

Using a global optimization algorithm, differential evolution, we show that optically actuated terahertz (THz) modulators can be designed using multilayers of InAs and GaSb semiconductors. The optical pump is actually able to generate photocarriers which diffuse into the structure and drastically modify its response at THz frequencies. A modulation depth between 63% and 85% is reached in the range of 1-3 THz for a low pump irradiance of 10 W cm(-2) in the continuous regime, whether the modulators operate at one or up to four arbitrary chosen frequencies.

关键词： terahertz modulator photogeneration differential evolution algorithms

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QANA: Quantum-based avian navigation optimizer algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104314-104314页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW Australia

differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems' dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into mull flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively.

关键词： Optimization Metaheuristic algorithms differential evolution algorithms Topology Large-scale global optimization Quantum computing Engineering optimization problems

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Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第10期15卷

作者： Zhang, Yu Zhang, Jiawen Luo, Lin Gao, Xiaorong Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 610031 Sichuan Peoples R China

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg-Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg-Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg-Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg-Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

关键词： Wheel-set size prediction neural network model Levenberg-Marquardt algorithm differential evolution algorithms

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Research on the Multi-population differential evolution Algorithm and the Performance

Research on the Multi-population Differential Evolution Algo...

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International Conference on Mathematics, Modeling and Simulation Technologies and Applications

作者： Renquan Huang Jing Tian Juanjuan Wang Junmin Kang School of Economy and Finance Xi'an International Studies University

ISBN: (纸本)9781713800361

The original differential evolution algorithm(DE) is a single-population differential evolution algorithm(SPDE).DE converges very quickly,and takes the advantage of *** improved DE has a better performance,but there are premature problems in optimizing complex *** multi-population differential evolution algorithm(MPDE) is proposed to overcome premature problems in this *** optimal substitution strategy(OSS) and the elite immigration strategy(EIS) are studied to maintain the diversity of *** simulation concludes that MPDE converges faster than SPDE in optimizing the ultra-high dimensional problems,and the EIS is superior to the ***,the efficiency of DE is more effective than that of MPDE when the algorithms *** shows that multi-population strategy is a feasible and effective way to the premature problems of DE.

关键词： Multi-population differential evolution algorithm Optimization algorithm Optimal substitution strategy Elite immigration strategy Optimization algorithms differential evolution algorithms evolutionary algorithm MT ENVIRONMENTAL IMPACT STATEMENTS open source software Office of the Supervising Scientist

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