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Height Sensitive Multi-UAV Deployment Scheme in Edge Data Acquisition System

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第6期33卷 2450105-2450105页

作者： Liu, Yichuan Tu, Jinbin Wang, Yun Southeast Univ Sch Comp Sci & Engn Key Lab Comp Network & Informat Integrat MOE Nanjing Peoples R China

In data acquisition scenario of edge computing, the optimization of UAV (Unmanned Aerial Vehicle) deployment is of great significance for making use of resources of UAV. We establish an optimization model of UAV cluster deployment in the edge data acquisition system. The model takes the height of UAV as the solving variable, which is more in line with the realistic characteristics. DEVIPSK-SA-FWA is proposed according to the characteristics of this model. The algorithm uses a novel coding mechanism, and uses K-Means to accelerate the convergence process of the algorithm. A variety of differential evolution mutation operators are used to form a self-adaptive strategy pool mechanism to carry out variable scale variation of population, which complete the global search well. Then fireworks algorithm searches the population locally after each round of global search. In our algorithm, global search and local search are well balanced and local optimal is effectively escaped. Finally, experimental results indicate that DEVIPSK-SA-FWA is capable of solving the model with good results, and the superiority of DEVIPSK-SA-FWA is verified through the Wilcoxon rank sum test method. In the best case, the proposed algorithm reduces energy consumption of edge data acquisition system by 32.87%.

关键词： Edge computing UAV K-Means self-adaptive strategy pool differential evolution algorithm fireworks algorithm

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Online public opinion prediction based on a novel conformable fractional discrete grey model

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KYBERNETES 2024年第13期53卷 72-100页

作者： Feng, Feng Ge, Xiaoxiao Tomasiello, Stefania Zhang, Jianke Xian Univ Posts & Telecommun Sch Sci Xian Peoples R China Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Peoples R China Univ Salerno Dept Ind Engn DIIN Salerno Italy

PurposeAs social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion ***/methodology/approachFirst, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about "IG team winning the championship". Finally, we conduct empirical analysis on two hot online public opinion events regarding "Chengdu toddler mauled by Rottweiler" and "Mayday band suspected of lip-syncing," to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey *** test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development t

关键词： Grey model Conformable fractional operator Social network differential evolution algorithm Public opinion

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Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

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IEEE ACCESS 2024年 12卷 33486-33503页

作者： Cheng, Yi Xie, Zhijun Xin, Yu Chen, Kewei Zarei, Roozbeh Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Fac Mech Engn Ningbo 315211 Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia

In a practical development environment, completing a job often necessitates the coordinated allocation of multiple resources. However, resource constraints such as molds are rarely considered in the multi-resource job shop scheduling problem. These molds have sequence-dependent setup times and are fully occupied during processing. Therefore, this study focuses on the Flexible Job Shop Scheduling Problem with Mold Resource Setup Time Optimization (FJSP-MRST), which aims to minimize setup time and makespan. Considering the characteristics of mold setup time, this paper introduces a Mold-Machine Double Fusion Insertion Method to optimize the mold-machine usage order and improve the mold change time calculation method. This innovative approach improves the overall scheduling process by efficiently managing the utilization of molds and machines. Furthermore, we propose a Mold-Operation Layer Loading Method to compress setup time, offering a novel strategy for reducing the setup time for mold resources. These two pioneering methods are integrated with an enhanced multi-objective differential evolution algorithm to obtain the optimal solution for FJSP-MRST. Experimental results demonstrate that the proposed methods can render the arrangement of machines and molds under the current process sequence more reasonable and effectively eliminate implicit setup time.

关键词： Job shop scheduling Biological cells Optimization Machining Loading Codes Sorting evolutionary computation Resource management Optimization methods Fusion power generation Flexible job shop scheduling problem mold resource setup time differential evolution algorithm

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A new deep neural network for forecasting: Deep dendritic artificial neural network

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第7期57卷 171-171页

作者： Egrioglu, Erol Bas, Eren Giresun Univ Fac Arts & Sci Dept Stat Forecast Res Lab TR-28200 Giresun Turkiye

Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures. It is available in the literature that the use of multiplicative aggregation functions in shallow artificial neural networks produces successful results for the forecasting problem. A type of high-order shallow artificial neural network that uses multiplicative aggregation functions is the dendritic neuron model artificial neural network, which has successful forecasting performance. In this study, the transformation of the dendritic neuron model turned into a multi-output architecture. A new dendritic cell based on the multi-output dendritic neuron model and a new deep artificial neural network is proposed. The training of this new deep dendritic artificial neural network is carried out with the differential evolution algorithm. The forecasting performance of the deep dendritic artificial neural network is compared with basic classical forecasting methods and some recent shallow and deep artificial neural networks over stock market time series. As a result, it has been observed that deep dendritic artificial neural network produces very successful forecasting results for the forecasting problem.

关键词： Forecasting Deep artificial neural networks differential evolution algorithm Dendritic neuron model

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A Novel Constraints Handling Method for Economic Dispatch with Valve-Point Effects

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第5期19卷 2875-2887页

作者： Li, Yixuan Guangxi Univ Dept Elect Engn 100 Daxue East Rd Nanning Guangxi Peoples R China

Economic dispatch with valve-point effects is a nonlinear and nonconvex problem in power systems. In addition to the optimization algorithm, the handing method of constraints including equality and inequality is one of the important factors for solving the economic dispatch problem. This paper presents a novel constraints handling method for solving economic dispatch with valve-point effects, consisting of the power repair strategy and the adaptive penalty function. The power repair strategy modifies the generator output power according to the characteristics of the valve-point. The penalty function changes according to the fitness value of the objective function. The method is applied to the economic dispatch problem solved by the standard differential evolution algorithm, and tested in 13-unit, 40-unit, 80-unit, 140-unit systems. The simulation results show that the proposed method can effectively increase the stability of convergence and improve the accuracy of solution for solving economic dispatch problem with valve-point effects.

关键词： Economic dispatch Valve-point effects Constraints handling method differential evolution algorithm

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Realization of Robust Frequency Stability in Low-Inertia Islanded Microgrids With Optimized Virtual Inertia Control

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IEEE ACCESS 2024年 12卷 58208-58221页

作者： Hamanah, Waleed M. Shafiullah, Md. Alhems, Luai M. Alam, Md. Shafiul Abido, Mohammad A. KFUPM Appl Res Ctr Metrol Stand & Testing Res & Innovat Dhahran 31261 Saudi Arabia KFUPM Coll Engn & Phys Dept Elect Engn Dhahran 31261 Saudi Arabia KFUPM Coll Engn & Phys Control & Instrumentat Engn Dhahran 31261 Saudi Arabia KFUPM Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia KFUPM Appl Res Ctr Metrol Stand & Testing Res & Innovat Dhahran 31261 Saudi Arabia KFUPM SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran 31261 Saudi Arabia

The increase in renewable energy resources penetration into microgrids weakens the inertia of microgrids and negatively affects their frequency stability. This reduction in inertia reduces the microgrid's ability to handle disturbances and increases frequency instability risk. The purpose of this paper is to present a virtual inertia (VI) control strategy for an island microgrid with the use of an energy storage system (ESS) to improve inertia and frequency stability of the system. The VI controller parameters are optimized using the differential evolution (DE) algorithm. The proposed VI control strategy can effectively improve microgrid frequency stability and reduce the risk of frequency instability. The results demonstrate that the proposed strategy is effective and can provide fast and reliable virtual inertia support for the microgrid. The proposed VI control approach is verified through simulation results and performs better than the conventional inertial response for frequency stability in islanded microgrids. Virtual inertia control injects/absorbs active power into/from the microgrid during frequency deviations. Different disturbances are applied to demonstrate uncertain load and RES generation behavior. The proposed VI control method is compared with the available literature. In the proposed method, the frequency deviation is better controlled. Furthermore, the results demonstrate a more stable and efficient system operation with the developed VI controller than the existing methods.

关键词： Virtual inertia control differential evolution algorithm energy storage systems microgrid renewable energy resources

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DHRDE: Dual-population hybrid update and RPR mechanism based differential evolutionary algorithm for engineering applications

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 431卷

作者： Hu, Gang Gong, Changsheng Shu, Bin Xu, Zhiqi Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Shaanxi Xueqian Normal Univ Sch Math & Stat Xian 710061 Peoples R China Univ N Carolina Pembroke NC 28372 USA

In this paper, an enhanced differential evolution algorithm based on dual population hybrid update and random population replacement strategy (namely RPR mechanism) is proposed, which is called DHRDE. DHRDE algorithm involves three key improvements, first, the elite reverse population is constructed according to the original population before the update phase to uncover more potential areas to be searched. Second, a perturbation mechanism is integrated into the DE/rand/2 approach of the differential evolution algorithm to bolster its search efficiency, two updating models are established using co-leadership of random and locally optimal individuals, and then dual-population hybrid update strategy is adopted to achieve all-round and multi-angle search. Thirdly, using RPR mechanism to operate multiple types of mutations on some populations further improves the convergence accuracy. In order to verify the effectiveness of the proposed algorithm, DHRDE is compared with a variety of different types of algorithms in multi-dimension of the CEC2017, CEC2020 and CEC2022 test set, and statistical analysis is performed by Wilcoxon rank sum test and Friedman test. The results show that DHRDE algorithm has better performance. DHRDE algorithm is also used to solve seven engineering design problems and three PV model parameter estimation problems, the optimization results show that DHRDE algorithm is suitable for different complex problems and has effectiveness. In addition, this paper establishes a smooth path planning model for multi-size robots, and uses DHRDE to solve the model, the results of five groups of simulation experiments show that DHRDE algorithm can provide robot moving trajectories with higher smoothness and shorter paths. Analyzing and comparing the fitness metrics through heat maps, the comparative study demonstrates that the DHRDE algorithm is more advantageous and stronger than other algorithms in solving the smooth path planning model for multi-size robots. Th

关键词： differential evolution algorithm Elite reverse population Hybrid update strategy RPR mechanism Engineering design problem Multi-Size Robots Smooth path planning

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A Hybridization of CSA DEA Approach for Detection of Multiple Transverse Crack Rotor Shaft Rotating in Fluid Environment under Axial and Bending Loading

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MECHANICS OF SOLIDS 2024年第4期59卷 2100-2126页

作者： Yadao, Adik Mishra, Kundan G H Raisoni Coll Engn & Management Pune 412207 Maharashtra India MIT Acad Engn Sch Mech Engn Pune India

Rotor shaft cracks can be acknowledged as a substantial issue regulating the reliable and safe machines operation. When the crack has grown and is not discovered, unexpected catastrophe may happen, ultimately resulting the plant being shut down with numerous losses attached. When the rotor shaft is revolving in a viscous fluid environment, it becomes exceedingly challenging to analyze the size and position of the crack. In the present research work, a novel hybridized technique a clonal selection algorithm (CSA) with a differential evolution algorithm (DEA) is proposed for early detection of multiple transverse cracks and its position in the rotor shaft rotating under altered viscous fluid environment in an acceptable amount of time limit. Relative natural frequency at x-axis, relative natural frequency at y-axis, relative amplitude of vibration at x-axis, relative amplitude at y-axis and viscosity of fluid are use as input parameter and relative crack depth and crack location are used as a output parameter in the hybrid technique. To compute the amplitude and natural frequency of the cracked rotor shaft rotating under different fluid environment utilizing the stiffness matrices of crack element. An external force of fluid is computed by Navier-Stokes equation. Theoretical evaluation was executed using Matlab. To authenticate the theoretical and experimental value of natural frequency and amplitude, perform the finite element analysis by using ANSYS. For training the hybrid system, the amplitude and natural frequencies are found out using theoretical, experimental and finite element analysis for different crack depth and positions. The test results of the recommended hybrid technique are compared with finite element analysis and experimental analysis for validation, and satisfactory outcomes have been observed. Therefore the recommended hybridized CSA-DEA technique would establish an effective tool for real-time crack detection in rotor.

关键词： rotor dynamics vibration signature crack depth crack location finite element analysis clonal selection algorithm differential evolution algorithm hybrid CSA-DEA

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Adaptive Framing and Virtual Channel Scheduling algorithm Based on Advanced Orbiting System for Consumer Sustainability in Industry 5.0

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第2期70卷 4882-4893页

作者： Bie, Yuxia Zhang, Zhongyuan Srivastava, Gautam Hu, Zhi Laghari, Asif Ali Sampedro, Gabriel Avelino Abbas, Sidra Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 404 Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut 03797 Lebanon Shenyang Normal Univ Software Coll Shenyang 110134 Peoples R China Univ Philippines Open Univ Fac Informat & Commun Studies Banos 4031 Philippines Salle Univ Ctr Computat Imaging & Visual Innovat Manila 1004 Philippines COMSATS Univ Islamabad Dept Comp Sci Islamabad 56300 Pakistan

Based on the development needs of Industry 5.0, Advanced On-orbit Systems (AOS) can be integrated with terrestrial 5G networks, with large-scale Internet of Things (IoT) links as well as with flexible deployment and resource optimization, enabling efficient transmission of multiple types of industrial data, human-machine collaboration, and improving the flexibility, innovation as well as efficiency of production processes. This paper first proposes an AOS adaptive framing algorithm based on an optimization threshold. The algorithm adaptively adjusts frame waiting time according to packet arrival conditions and optimizes the frame waiting time threshold using a differential evolution algorithm. Furthermore, an AOS virtual channel scheduling algorithm based on a Deep QNetwork (DQN) is proposed. The algorithm considers the service priority, scheduling delay and frame residual to find the optimal virtual channel scheduling order. Through simulation, it can be seen that the adaptive framing algorithm based on optimized threshold values can effectively reduce average framing time and average packet delay while ensuring efficiency of frame reuse. Moreover, the virtual channel scheduling algorithm based on DQN can better meet the needs of the network, effectively reducing average scheduling delay and frame residual. The combination of AOS framing and virtual channel scheduling can improve transmission efficiency and optimize system performance.

关键词： Scheduling Delays Scheduling algorithms Multiplexing Job shop scheduling Heuristic algorithms Adaptation models AOS adaptive frame generation algorithm differential evolution algorithm virtual channel scheduling DQN

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A Fuzzy Portfolio Selection Model with Cardinality Constraints Based on differential evolution algorithm

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2013年第9期39卷 130-137页

作者： He Jiandong Jiaxing Vocat Tech Coll 1123 Changsheng South Rd Jiaxing Zhejiang Peoples R China

It is difficult to estimate the expected rates of security returns precisely for many uncertainties. In this paper, we use fuzzy numbers to describe the uncertainty, and use the possibilitic mean value and variance to describe return and risk. And then a nonlinear mixed-integer programming with cardinality constraints is established and solved by a Modified differential evolution algorithm. Finally, an applied example is given to illustrate the effectiveness and reliance of the model.

关键词： portfolio selection fuzzy numbercardinality constraints differential evolution algorithm

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