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Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

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MEASUREMENT 2022年 199卷

作者： Zhang, Guangcai Wan, Chunfeng Xiong, Xiaobing Xie, Liyu Noori, Mohammad Xue, Songtao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tongji Univ Res Inst Struct Engn & Disaster Reduct Coll Civil Engn Shanghai Peoples R China Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA USA Tohoku Inst Technol Dept Architecture Sendai Japan

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Le acute accent vy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

关键词： Structural damage identification Jaya algorithm differential evolution algorithm Hybrid algorithm Adjacent acceleration correlation function Gradient search method

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Multi-objective optimum design of five-phase squirrel cage induction motor by differential evolution algorithm

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ENERGY REPORTS 2022年 8卷 51-62页

作者： Chen, Hansi Zhao, Jinghong Wang, Hanming Zhang, Qingfei Luo, Xiangyu Xu, Hao Xiong, Yiyong Naval Univ Engn Coll Elect Engn Wuhan 430033 Hubei Peoples R China

Compared with the traditional induction motors, five-phase squirrel cage induction motor (FSCIM) have the advantages of lower torque ripple and single-phase power, flexible control strategy, higher power density and fault-tolerant operation, which has been widely applied on electrical vehicles, rail transit, underwater vehicles and so on. This paper presents an analytical design optimization method based on the coupling of electromagnetic equivalent circuit (EEC) and differential evolution algorithm (DEA). Furthermore, the difference between three and five-phase winding on harmonic specific leakage permeance is investigated in detail. The nonlinear and oeolotropic influences of design geometrical parameters (slots openings, main dimension ratio, yoke height, airgap length, etc.) on the performances of the basic FSCIM model are evaluated with analytical model. For more accurate results, the harmonic characteristics of five-phase winding structure, iron core saturation, rotor slot skew width, stator end winding, slot leakage inductance and stator and rotor slot structure are considered. Meanwhile, the efficiency, power factor, maximum torque, slot fill factor and material consumption of FSCIM are calculated as the comprehensive objective function. The transient finite element analysis (TFEA) and experimental test verify the accuracy of the proposed optimum model. In addition, the feasibility of the multi objective optimization design scheme provided by the analytical model is verified by TFEA. (c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Five-phase induction motor differential evolution algorithm Multi objective optimization Fast performance computation

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Online algorithm configuration for differential evolution algorithm

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APPLIED INTELLIGENCE 2022年第8期52卷 9193-9211页

作者： Huang, Changwu Bai, Hao Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China INSA Rouen Normandie Lab Mech Normandy LMN F-76000 Rouen France

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance.

关键词： Automatic algorithm configuration differential evolution algorithm Adaptive parameter control Multi-armed bandit Kernel density estimation Machine learning

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Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem

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INFORMATION SCIENCES 2022年 613卷 469-493页

作者： Liu, Di Hu, Zhongbo Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

The combined heat and power economic dispatch (CHPED) problem is a complicated non -convex optimization problem with several constraints that has been a challenging and important task in the power system. Great efforts have been made to solve the small-scale CHPED problem, and there is considerable scope for research in the large-scale CHPED problem with lots of local optima. This paper presents a neighborhood-based differ-ential evolution algorithm with direction induced strategy (NDIDE) to solve the large-scale CHPED problem. In NDIDE, a novel mutation strategy, named neighborhood nonelite direc-tion induced strategy based on DE/rand/1 (DE/ldrand/1), is proposed to explore some untrodden areas of a search space using the direction information of pointing to inferior solutions within a small neighborhood. Such a strategy helps to strengthen the exploitation of some selected neighborhoods. Consequently, a good balance between exploration and exploitation abilities could be achieved. NDIDE is implemented on the CHPED systems with 7, 84, 96 and 192 units. The dispatch schedules obtained from NDIDE are feasible in three large CHPED systems and show the advantages of NDIDE in terms of cost savings and the robustness over the results obtained by three state-of-the-art differential evolution algo-rithms as well as recent works in this field. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Combined heat and power (CHP) differential evolution algorithm Economic dispatch Large-scale system

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Novel Formulations and Improved differential evolution algorithm for Optimal Lane Reservation With Task Merging

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21329-21344页

作者： Wu, Peng Xu, Ling D'Ariano, Andrea Zhao, Yongxiang Chu, Chengbin Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Roma Tre Univ Dept Engn Rome Italy Univ Gustave Eiffel ESIEE Paris COSYS GRETTIA F-77454 Marnela La Vallee France

This paper investigates a new lane reservation problem with task merging that consists of optimally determining which lanes in a transportation network have to be reserved and designing reserved lane-based routes in the network for time-crucial transport tasks. Part of the tasks whose destinations are geographically close is merged to reduce the number of vehicles and transport costs. Reserved lanes can reduce the travel time of task vehicles passing through them, while they will generate negative impact on normal traffic, such as traffic delay to the vehicles on adjacent non-reserved lanes. The objective is to minimize the total negative impact of all reserved lanes. For this problem, two new integer linear programming (ILP) models are first developed. The complexity of the problem is proved to be NP-hard. Since commercial solver (like CPLEX) is time-consuming for solving it when the problem size increases, a fast and effective improved differential evolution algorithm (IDEA) is developed based on explored problem properties. Extensive experimental results for a real-life case and benchmark instances of up to 500 nodes in the network and 30 transport tasks show the favorable performance of the IDEA, as compared to CPLEX, differential evolution algorithm and genetic algorithm. Management insights are also drawn to support practical decision-making.

关键词： Task analysis Roads Transportation Merging Costs Games Uncertainty Lane reservation task merging integer linear programming differential evolution algorithm

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Research on Supermarket Vegetable Sales Optimization Based on Grey Prediction Model and differential evolution algorithm 3

Research on Supermarket Vegetable Sales Optimization Based o...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Wang, Yixiao Jing, Haoyu Wang, Lin Xinjiang University Urumqi China

ISBN: (纸本)9798350380989

This paper mainly focuses on the comprehensive research of vegetable sales data in a supermarket, using statistical analysis, predictive modeling, and optimization algorithms to explore the distribution of vegetable sales, pricing strategies, and replenishment planning. Firstly, by analyzing the category sales data of different years, it is found that holidays have a significant impact on vegetable sales, and the grey correlation between each category is strong. Secondly, based on the cost-plus pricing theory, the ARIMA model is used to predict future sales, and the replenishment quantity and sales pricing of each category are determined by single-objective programming. Finally, considering the actual constraints, the grey prediction model and improved differential evolution algorithm are used to optimize the purchase quantity and pricing strategy of single item. The study of this paper provides scientific decision support for supermarket vegetable sales and helps to improve sales efficiency and economic benefits. © 2024 IEEE.

关键词： differential evolution algorithm Grey Prediction Model Pricing Strategy Replenishment Planning Vegetable Sales Distribution

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Structural Design and Optimization of a Resonant Micro-Accelerometer Based on Electrostatic Stiffness by an Improved differential evolution algorithm

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MICROMACHINES 2022年第1期13卷 38-38页

作者： Huang, Libin Li, Qike Qin, Yan Ding, Xukai Zhang, Meimei Zhao, Liye Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Minist Educ Key Lab Microinertial Instruments & Adv Nav Techn Nanjing 210096 Peoples R China

This study designed an in-plane resonant micro-accelerometer based on electrostatic stiffness. The accelerometer adopts a one-piece proof mass structure;two double-folded beam resonators are symmetrically distributed inside the proof mass, and only one displacement is introduced under the action of acceleration, which reduces the influence of processing errors on the performance of the accelerometer. The two resonators form a differential structure that can diminish the impact of common-mode errors. This accelerometer realizes the separation of the introduction of electrostatic stiffness and the detection of resonant frequency, which is conducive to the decoupling of accelerometer signals. An improved differential evolution algorithm was developed to optimize the scale factor of the accelerometer. Through the final elimination principle, excellent individuals are preserved, and the most suitable parameters are allocated to the surviving individuals to stimulate the offspring to find the globally optimal ability. The algorithm not only maintains the global optimality but also reduces the computational complexity of the algorithm and deterministically realizes the optimization of the accelerometer scale factor. The electrostatic stiffness resonant micro-accelerometer was fabricated by deep dry silicon-on-glass (DDSOG) technology. The unloaded resonant frequency of the accelerometer resonant beam was between 24 and 26 kHz, and the scale factor of the packaged accelerometer was between 54 and 59 Hz/g. The average error between the optimization result and the actual scale factor was 7.03%. The experimental results verified the rationality of the structural design.

关键词： resonant accelerometer electrostatic stiffness structural design differential evolution algorithm

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Optimizing Nutrient Combinations by differential evolution algorithm: A Computational Approach to Dietary Planning 4

Optimizing Nutrient Combinations by Differential Evolution A...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Bhargava, Deepshikha Tyagi, Neha Bhargava, Amitabh Amity School of Engineering & Technology Amity University Greater Noida India Amity Business School Amity University Greater Noida India

ISBN: (纸本)9798350387490

In this Paper we present a technique using differential evolution to optimize nutrient combinations in diet planning. The objective is to derive the best combination of components that satisfies particular nutritional demands while causing the slightest deviation from optimal ratios of carbohydrates, proteins, and fats. We started by generating a population of nutritional combinations and, through an iterative process, enhanced them using mutation and selection procedures. Our results illustrate how the algorithm converges to optimal nutrient distributions and is an effective optimization tool for diets. © 2024 IEEE.

关键词： Dietary Planning differential evolution algorithm Nutrient

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The Training of Pi-Sigma Artificial Neural Networks with differential evolution algorithm for Forecasting

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COMPUTATIONAL ECONOMICS 2022年第4期59卷 1699-1711页

作者： Yilmaz, Oguzhan Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkey

Looking at the artificial neural networks' literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward artificial neural networks models are performed with derivative-based algorithms such as levenberg-marquardt and back-propagation learning algorithms in the first studies. In recent years, although many new heuristic algorithms have been proposed for different aims these heuristic algorithms are also frequently used in the training process of many different artificial neural network models. Pi-sigma artificial neural networks have different importance than many artificial neural network models with its higher-order network structure and superior forecasting performance. In this study, the training of Pi-Sigma artificial neural networks is performed by differential evolution algorithm uses DE/rand/1 mutation strategy. The performance of the proposed method is evaluated by two data sets and seen that the proposed method has a very effective performance compared with many artificial neural network models.

关键词： Pi-Sigma artificial neural networks differential evolution algorithm Higher-order neural networks Forecasting

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A new differential evolution algorithm for cooperative fuzzy rule mining: application to anomaly detection

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evolutionARY INTELLIGENCE 2022年第4期15卷 2667-2678页

作者： Guendouzi, Wassila Boukra, Abdelmadjid Univ Algiers Benyoucef Benkhedda 02 Didouche Mourad Algiers Algeria Univ Sci & Technol Houari Boumediene Algiers Algeria

Fuzzy classification rule mining can be considered as a challenging optimization problem with the purpose of extracting accurate and interpretable rules. This paper deals with a Michigan cooperative approach for mining fuzzy rules. The proposed algorithm named EDE-FRMiner uses an enhanced differential evolution that evolves population of individuals;each one represents a single rule. The whole population collaborates to generate in one shot an accurate and reduced number of rules. EDE-FRMiner is an intelligent process of evolution that uses fast arithmetic operators and a new cooperative weights memory. This latter allows sharing information between individuals. In addition, it uses a new threshold based fitness function using a redefined support and confidence measures. The adaptive threshold mechanism used in the fitness function aims to adapt the miner system to problems with dynamic training data. Experiments are carried out using the NSL-kdd'99 intrusion detection data set and other data sets from the UCI repository. A comparative study with other competitive evolutionary rule based systems is performed and the results show the effectiveness of proposed algorithm.

关键词： evolutionary algorithm differential evolution algorithm Cooperative approach Combinatorial optimization problem Fuzzy association rule Intrusion detection

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