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Geometry optimization of auxetic honeycomb and dynamic analysis of sandwich plate and shell panels with variable stiffness composite laminates

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JOURNAL OF SANDWICH STRUCTURES & MATERIALS 2025年第4期27卷 759-789页

作者： Gupta, Krishan Kumar Pradyumna, S. Indian Inst Technol Delhi Dept Appl Mech Hauz Khas New Delhi 110016 India

In the present investigation, the fiber orientations and geometric parameters of the sandwich plate and shell panels with auxetic honeycomb core and curvilinear fiber-reinforced facesheets are optimized for the maximum fundamental frequency. The dynamic analysis is carried out considering a higher-order shear deformation theory and employing a C0 eight-noded isoparametric element with nine degrees of freedom per node. The artificial bee colony algorithm is used to optimize different parameters of the auxetic core of the sandwich panel. The mechanical properties of the original material and the geometrical features of the unit cells are used to determine the mechanical properties of the auxetic core. The effects of fiber orientations, and geometric parameters like rib thickness, inclined cell angle, vertical cell rib length, and inclined cell rib length of the core on maximizing fundamental frequency parameters of the panels are investigated. It is evident from the present study that the natural frequency of the panels can be altered without changing their plan dimensions and mass by suitably altering the geometric parameters of the auxetic honeycomb core. The analysis is extended for the different ratios of the density of facesheets and auxetic core materials. The analysis is carried out for three and five-layer sandwich panels with different facesheet-auxetic core arrangements and boundary conditions. For five layered auxetic honeycomb sandwich plates and shell panels, the non-dimensional fundamental frequency is observed to be lower for sandwich with alternate facesheet-core arrangement i.e., three facesheets and two core layers, compared to that of a single core at the middle. The non-dimensional frequency parameter is also found to be sensitive to geometry parameters of auxetic core and fiber angles of the facesheets.

关键词： Optimization auxetic materials honeycomb sandwich dynamic analysis variable stiffness composite laminates (VSCL) higher-order theory artificial bee colony algorithm

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A multi-granular general evolutionary computation framework by fully utilizing the eliminated particles

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第5期15卷 1927-1948页

作者： Xiong, Yiping Xia, Shuyin Li, Caoxiao Lian, Xiaoyu Hou, Bin Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Peoples R China

In the evolutionary algorithm, an intuitive phenomenon is that the eliminated bad particles are also beneficial to convergence in evolutionary algorithms by preventing the generated particles from being close to those eliminated bad particles. Most existing algorithms do not take full advantage of the historical information of these particles or use surrogate models without guaranteeing approximation accuracy. In this study, we propose a multi-granularity general framework to divide the feasible region into different granularities by utilizing completely random trees and computing the spatial distribution of individuals. Secondly, through the sampling and migration strategy, make full use of the sparsity of the calculated individual space distribution and the locality of the best individual in history to replace the poor individual in the current population to speed up the local convergence speed of the algorithm. The time complexity of the algorithm using this framework is equal to the maximum between the time complexity of the evolutionary algorithm using this framework and O(tMlogM), where M denotes the number of points and historical particles generated in an iteration and t denotes the number of iterations. Therefore, the additional computational cost incurred by this framework is very low. Experiments on 12 classical functions, including high-dimensional functions, show that the proposed framework can improve four respective evolutionary algorithms and achieve significantly better results in terms of convergence performance and optimization accuracy.

关键词： Evolutionary computation Evolutionary algorithms Genetic algorithm Firefly algorithm Differential evolution algorithm artificial bee colony algorithm

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Constraint relaxation active thermal management strategy under multi-source perturbations to enhance fuel cell vehicle's output power and voltage consistency

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 102卷 332-347页

作者： Cao, Jishen Yin, Cong Wang, Renkang Qiao, Zemin Tang, Hao Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Hydrogen & Fuel Cell Inst Chengdu 611731 Peoples R China

Active thermal management strategies are critical for optimizing fuel cell performance by regulating stack temperature in response to output power variations. However, existing approaches often fail to adequately consider the impact of multi-source perturbations, such as gas supply perturbations or voltage distribution heterogeneity. To bridge this gap, we propose a nonlinear autoregressive exogenous network surrogate model to simulate fuel cell voltage distribution. This model is integrated into an advanced online thermal management control system. The proposed strategy employs an artificial bee colony optimization algorithm and a tube-based robust model predictive control strategy with relaxation factors. It enables real-time regulation of coolant outlet and inlet temperatures in response to variations in load current, reactant gas flow rate and pressure, and voltage distribution characteristics. Experimental results demonstrate that at a current density of 1.0 A/cm2, the strategy increased the average cell voltage by 6.1 mV, reduced the voltage extreme difference by 40.3%, and lowered the voltage standard deviation by 54.9%. The active thermal management strategy significantly enhances the performance of fuel cells under multi-source perturbations.

关键词： Proton exchange membrane fuel cell Nonlinear autoregressive exogenous network artificial bee colony algorithm Tube-based robust model predictive control Thermal management system

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Designing the optimal path curve based on spline functions for mobile robot using the combination of bee colony algorithm and genetic algorithm

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JOURNAL OF VIBRATION AND CONTROL 2025年第7-8期31卷 1359-1376页

作者： Min Lin, Xing Xia, Luting Ge, Zhen Hong, Xinhui Zhao, Ruoyi Jalalnezhad, Mostafa Fuzhou Univ Xiamen Acad Arts & Design Xiamen Peoples R China Zhejiang Gongshang Univ Hangzhou Coll Commerce Hangzhou Peoples R China Jiangnan Univ Sch Design Wuxi Peoples R China Zhejiang Gongshang Univ Dept Art Design Hangzhou Peoples R China Kharazmi Univ Dept Mech Engn Tehran 15614 Iran

This research focuses on the routing algorithm for mobile robots, considering factors such as time, path length, and energy consumption. Various algorithms have been used to plan trajectory curves, including classical methods, probabilistic algorithms, and meta-heuristic algorithms. The artificial bee colony algorithm was used to generate practical path curves without collisions, which were then used to create the optimal path length. The basic model was a projection of the path curve using random initial paths. Spline interpolation was used to obtain the middle points of the path, making the path smooth. The proposed model has less time and length in all environments and better satisfies linear and rotational speed limitations, indicating potential for improvement.

关键词： Mobile robots routing trajectory curve projection artificial bee colony algorithm genetic algorithm

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A multi-period asymmetric transit frequency design problem

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Gong, J. Szeto, W. Y. Sun, S. Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat Shenzhen Peoples R China Guangdong Hong Kong Macau Joint Lab Smart Cities Hong Kong Peoples R China Jinan Univ Inst Phys Internet Sch Intelligent Syst Sci & Engn Zhuhai Campus Zhuhai Peoples R China

Transit frequency design is critical in determining the performance of public transit services. In the literature, single-period frequency design is often considered but ignores the demand variation over time of day. Moreover, in high-demand bus networks, the demand patterns are asymmetric in both directions of some bus routes. This study investigates a bus operation strategy to address these two issues. In this strategy, for each route, a class of buses serves both directions while the other class only serves one direction with high travel demand, leading to the two directions having different frequencies. A bilevel optimization problem is formulated for this strategy. The upper level problem is a multi-period asymmetric transit frequency design problem, which aims to determine the route frequencies of different classes of buses associated with each period to maximize the operating profit or social welfare. This upper level problem also considers deadhead trips between the bus depot and terminals or between terminals of different routes across periods. The lower level problem is a schedule-based user equilibrium transit assignment problem, taking elastic demand, the common line choice of passengers, and capacity constraints into account. A hybrid algorithm combining an enhanced artificial bee colony algorithm with the method of successive averages is proposed to tackle the bilevel optimization problem and then applied to the study of the Tin Shui Wai bus network to demonstrate the model properties. The effectiveness of the proposed algorithm is also examined. The results indicate that the proposed algorithm can produce better solutions compared with the modified hybrid genetic algorithm. Moreover, the proposed multi-period asymmetric design outperforms the existing design, which can achieve less passenger travel time and greater demand satisfaction, operating profit, and social welfare.

关键词： Asymmetric transit frequency setting Multi-period Bilevel optimization artificial bee colony algorithm

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A two-stage Duffing equation-based oscillator and stochastic resonance for mechanical fault diagnosis

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CHAOS SOLITONS & FRACTALS 2024年 182卷

作者： Xiang, Jiawei Guo, Jianchun Li, Xiaoqi Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

Incipient fault-induced weak impulses will not be detected easily using the existing fault extraction methods. Stochastic resonance can flexibly use noises to enhance the signal-to-noise ratio, but it lacks the ability of antiinterference to obtain robustness and reliability fault detection performance. A two-stage method is presented to diagnose faults in mechanical components. The first is to judge the fault existence according to the characteristics of the phase space trajectory diagram of the Duffing oscillator. The second is to employ stochastic resonance method based on the Duffing equation to improve the signal-to-noise ratio of the raw signal, in which the two barrier parameters in the Duffing equation-based bistable system are carefully selected using the artificial bee colony algorithm. In the present two-stage method, the existence of faults and further the fault types in mechanical components are stepwise detected to make full usages of the advantages of Duffing oscillator and Duffing equation-based stochastic resonance method. Numerical simulations and two experimental cases verify the performance of the proposed method for mechanical components fault detection in a strong noise environment.

关键词： Duffing oscillator Stochastic resonance artificial bee colony algorithm Bearings and gears Fault detection

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Increasing the Fitting Precision of Implicit Polynomial Curves in 2D Modeling Through Signomial Powers

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IEEE ACCESS 2024年 12卷 69909-69921页

作者： Pence, Ihsan Burdur Mehmet Akif Ersoy Univ Dept Software Engn TR-15300 Burdur Turkiye

Today, common algorithms such as spline curves and triangulation methods are used in 2-and 3-dimensional modeling of objects. However, although these methods can express objects well in terms of visuality because they treat objects as a combination of many small parts, they cannot reveal the mathematical expression of the whole object. The most prominent method of expressing objects with a single equation is the implicit polynomial (IP) curves and the 3L method used to realize the more precise effects of these curves. In this study, the 3L method was accepted as the primary method, and signomial functions using real number powers were used to increase the fitting precision in 2D modeling. The artificial bee colony (ABC) algorithm was used to determine signomial terms. Based on the root mean square error, peak signal-to-noise ratio, coefficient of determination, and structural similarity values obtained in experimental studies, modeling success can be increased. Also, due to adding the initial power expression of IP as the population element for ABC, the sensitivity of the current IP model is increased rather than remodeled.

关键词： Mathematical models Polynomials IP networks Shape Fitting Three-dimensional displays Solid modeling artificial bee colony algorithm Signomial functions implicit polynomial curves 3L method artificial bee colony modeling

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Intelligent ADRC-based inertia control for offshore wind farm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2024年第3期18卷 530-541页

作者： Li, Tai Sun, Sunan Li, Mengjie Cheng, Liang Ji, Zhicheng Shen, Yanxia Wuxi Univ Sch Automat Wuxi Jiangsu Peoples R China Jiangsu Univ Sci & Technol Coll Automat Zhenjiang Jiangsu Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China WuXi Univ Sch Automat Wuxi 214105 Peoples R China

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non-linear feedback control law and the extended state observer to improve the anti-interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC-ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti-interference ability, but also enhance the robustness of the system. This paper proposes a wind farm virtual inertia control strategy based on auto disturbance rejection controller with bee colony algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller (ADRC) is designed according to the non-linear feedback control law and the extended state observer (ESO) to improve the anti-interference capability of the virtual inertia control system. Second, the bee colony algorithm based on nectar-collecting behaviour is presented to be iteratived to optimize the ADRC parameters, and to realize the power sharing and adaptive noise cancellation. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with the traditional algorithm, the proposed ABC-ADRC can adjust the frequency of the pow

关键词： active disturbance rejection control artificial bee colony algorithm wind power

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A Self-Adaptive Differential artificial bee colony for Combined Economic Emission Dispatch of Power System

A Self-Adaptive Differential Artificial Bee Colony for Combi...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Nurul Ilmi Ardiaty Arief Hasniaty A. Muhammad Bachtiar Nappu Department of Electrical Engineering Hasanuddin University Gowa South Sulawesi Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

The energy sector heavily relies on fossil-fueled thermal power plants, significantly contributing to operational costs and greenhouse gas emissions. Optimizing these costs while reducing emissions remains a critical challenge in modern energy management. Dual-objective optimization methods are often complex due to their conflicting nature. This paper transforms the dual-objectives into a single-objective framework called Combined Economic Emission Dispatch (CEED) to address this issue. This study presents the self-adaptive differential artificial bee colony (sdABC) algorithm as a novel approach to addressing the CEED problem in power systems. The sdABC algorithm enhances the classical artificial bee colony (ABC) method by integrating differential evolution (DE) strategies and self-adaptive mechanisms. These advancements enable the algorithm to adapt dynamically to complex optimization landscapes, thereby ensuring accelerated convergence and improving the efficiency of solution space exploration. Simulations on the IEEE 30-bus test system were conducted to assess the effectiveness of the proposed algorithm, considering transmission losses and operational constraints. The analysis confirms that the sdABC algorithm outperforms the classical ABC algorithm by achieving lower generation costs and reduced emissions.

关键词： Renewable energy sources Technological innovation Costs Heuristic algorithms Transforms Wind power generation artificial bee colony algorithm Energy management Sustainable development Convergence

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Robust Beamforming for UAV Communication With Jittering Effects

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 48-52页

作者： Lee, Kyuyeon You, Dukhyun Noh, Hoondong Lee, Chungyong Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea Elect & Telecommun Res Inst Satellite Commun Res Div Daejeon 34129 South Korea

We propose a robust beamforming scheme for unmanned aerial vehicle (UAV) communication systems with jittering effects. To overcome the non-convexity of the problem, we propose an artificial bee colony (ABC) algorithm-based beamforming scheme and utilize sample average approximation to optimize the objective function, which is challenging to express in a closed form. Numerical results demonstrate that the proposed scheme significantly enhances outage performance and maintains high spectral efficiency across various antenna configurations and jitter levels. Our method demonstrates superior robustness against angle ambiguity caused by jitter compared to narrow beams and existing wide-beam techniques, resulting in enhanced overall performance.

关键词： Autonomous aerial vehicles Jitter Array signal processing Vectors artificial bee colony algorithm Fluctuations Three-dimensional displays Spectral efficiency Air to ground communication Mobile antennas Cellcular-connected UAV jittering effects robust beamforming artificial bee colony (ABC) algorithm

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