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A Water Level Forecasting Method Based on an Improved jellyfish search algorithm Optimized with an Inverse-Free Extreme Learning Machine and Error Correction

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WATER 2024年第20期16卷 2871页

作者： Zhang, Qiwei Shou, Weiwei Wang, Xuefeng Zhao, Rongkai He, Rui Zhang, Chu Power China Huadong Engn Corp Ltd Hangzhou 310000 Peoples R China Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, reducing flood risks, and promoting ecological and environmental protection, which is crucial for the sustainable development of society. This study proposes a hybrid water level forecasting model based on Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Inverse-Free Extreme Learning Machine (IFELM), and error correction. Firstly, historical water level data are decomposed into different modes using TVFEMD;secondly, the Improved jellyfish search (IJS) algorithm is employed to optimize the IFELM, and subsequently, the optimized IFELM independently forecasts each sub-sequence and obtains the predictive results of each sub-sequence;thirdly, an Online Sequential Extreme Learning Machine (OSELM) model is used to correct data errors, and the initial predictive results and error prediction results are added together to obtain the final prediction for the sub-sequence;and finally, the final prediction for the sub-sequences are added to obtain the prediction results of the entire water level sequence. Taking the daily water level data from 2006 to 2018 in Taihu, China as the research object, this paper compares the proposed model with the ELM, BP, LSTM, IFELM, TVFEMD-IFELM, and TVFEMD-IFELM-OSELM models. The results show that the TVFEMD-IJS-IFELM-OSELM model established in this study has high prediction accuracy and strong stability and is suitable for water level forecasting.

关键词： water level forecasting Time-Varying Filter-based Empirical Mode Decomposition jellyfish search algorithm Inverse-Free Extreme Learning Machine error correction

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A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement

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BUILDINGS 2025年第6期15卷 843-843页

作者： Tang, Yongsheng Lin, Yunzhen Yu, Tao Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Peoples R China China Airport Construct Grp Co Ltd Engn Res Ctr Intelligent Construct & Industrializa Tianjin 300456 Peoples R China

Due to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method based on a neural network is applied to implement a long-term structural assessment, with the input being monitored strain data;it is named the jellyfish search algorithm-optimized BP neural network (JS-BP) model. Considering the structural characteristics, three key parameters are selected as the key parameters to implement the assessment, namely, the bending and tensile modulus, reaction modulus at top of the subgrade, and seam equivalent modulus. To implement the method, the databases are established first with the simulation results from some finite element models of prefabricated concrete pavement. Then, the proposed JS-BP neural network model is trained and checked with the established database. The simulation results verify an excellent accuracy of the proposed method as the difference between the predicted value and the true value is smaller than 1%. Moreover, the aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the proposed JS-BP model, the accuracy of the traditional BP model is not so high, as the largest error can be up to 25%. Lastly, the proposed method is verified with some experiments using laboratory models. From the test results it is indicated that the prediction accuracy of the proposed method for the three parameters is still good enough, as the prediction error is within 5%.

关键词： prefabricated concrete pavement structural parameter inversion jellyfish search algorithm BP neural network

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A Chaotic Elite Cloning Artificial jellyfish algorithm for Efficient Task Allocation in IOTWSNs

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IEEE SENSORS JOURNAL 2025年第4期25卷 6905-6919页

作者： Wen, Dikun Cao, Qike Feng, Shourui Zhang, Zhehao Zhou, Peng Henan Univ Sch Software Kaifeng 475000 Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Peoples R China Sun Yat Sen Univ Sch Life Sci Guangzhou 510275 Peoples R China Henan Univ Econ & Law Sch Stat & Big Data Zhengzhou 450000 Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Urumqi 841000 Xinjiang Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

The Internet of Things wireless sensor networks (IOTWSNs) are crucial in modern smart systems, where self-organizing sensor nodes enable efficient and flexible network structures for applications like environmental monitoring and smart cities. The task allocation problem in IOTWSNs is NP-hard, making effective strategies essential for optimal network performance. This article proposes an improved artificial jellyfish search algorithm (CECJS) that integrates chaotic initialization, elite, and cloning strategies to enhance global search ability and convergence speed. To evaluate CECJS's efficiency, the article introduces network gain, reflecting both network effectiveness and task completion quality. Experimental results show that CECJS significantly outperforms traditional algorithms like genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) in task allocation gains, achieving improvements of several to tens of percentage points. In addition, CECJS exhibits faster convergence, finding near-optimal solutions more efficiently, making it an effective solution for large-scale IOTWSNs task optimization.

关键词： Resource management Sensors Intelligent sensors Cloning Optimization Heuristic algorithms Wireless sensor networks jellyfish Convergence Medical services IoT wireless sensor networks jellyfish search algorithm optimization algorithms performance evaluation task allocation

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Jelly fish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis

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ENERGY 2021年 221卷 119836-119836页

作者： Gouda, Eid A. Kotb, Mohamed F. El-Fergany, Attia A. Mansoura Univ Fac Engn Dept Elect Engn Mansoura Egypt Zagazig Univ Fac Engn Dept Elect Power & Machines Zagazig 44519 Egypt

In this paper, a novel attempt to employ the jellyfish search algorithm (JSA) for solving parameters' identifications problem of polymer exchange membrane fuel cells (PEMFCs) model is addressed. The minimization of the sum of squared errors (SSEs) between measured and estimated voltage dataset points define the fitness function to be optimized by the JSA subject to set of self-constrained inequality bounds. Three test cases are demonstrated complete with necessary verifications, comparisons and subsequent discussions. It can be quantified here that the best cropped values of SSEs are equal to 0.011699, 0.33598 and 2.14570 V-2 for BCS 500-W module, 250-W stack and NedStack type PS6 unit;respectively. It can be confirmed that the maximumvalue of percentage voltage biased error is +/- 1% for all test cases under study. In addition, various performance measures are made to signify the robustness of the cropped results. At a later stage, various operating principal characteristics under changeable temperatures and varied regulating pressures are illustrated. It can be established that the JSA proves its ability to tackle this problem competently compared to others. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Polymer exchange membrane fuel cells Parameter's identifications Optimization methods jellyfish search algorithm

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A comparison of swarm-based optimization algorithms in linear antenna array synthesis

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第4期20卷 1520-1531页

作者： Durmus, Ali Kurban, Rifat Karakose, Ercan Kayseri Univ Vocat Coll Dept Elect & Energy Kayseri Turkey Kayseri Univ Vocat Coll Dept Comp Technol Kayseri Turkey Kayseri Univ Engn & Architecture & Design Fac Dept Nat Sci Kayseri Turkey

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with a low side lobe level (SLL) at a desired half power beam width in far-field. The amplitude and position values of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper, swarm-based metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10-, 16-, 24- and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers, and statistical results show that the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

关键词： Linear antenna array synthesis Side lobe level Particle swarm optimization Artificial bee colony algorithm Mayfly algorithm jellyfish search algorithm

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Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation

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APPLIED ENERGY 2024年 373卷

作者： Zhang, Chengyu Rezgui, Yacine Luo, Zhiwen Jiang, Ben Zhao, Tianyi Dalian Univ Technol Inst Bldg Energy Dalian Peoples R China Univ Cardiff Sch Engn Cardiff Wales Cardiff Univ Welsh Sch Architecture Cardiff Wales

Community microgrids represent a pivotal solution for addressing energy conservation and reducing carbon emissions. However, few studies focused on the methods of concurrently optimizing both the supply and demand sides within campus settings, a unique form of community. Moreover, few studies on energy demand optimization rarely delved into human-centered approaches and employed multi-strategy parallel flexible regulations, while few studies on energy supply optimization discussed the comprehensive environment-energy-societyinvestment benefits as optimization objectives with suitable algorithms for calculation. Consequently, this study introduced a novel approach to energy system supply-demand optimization by simultaneous flexible energy-use regulation and microgrid operation scheduling, to explore human-centered multi-strategy parallel flexible energy-use regulations, and comprehensive evaluation parameters for energy supply-demand optimization. (1) Regarding flexible energy-use regulation, this study proposed several adjustments including adjusting the start-up temperature of air conditioners (ACs), organized charging of electric vehicles (EVs), light or plug management, and course optimization. These parallel adjustments can reduce or shift 12.12% of energy demand during peak hours and 8.06% during sharp hours, yielding 4.449% - 8.820% of the annual average benefits in different cases. (2) Regarding microgrid optimization scheduling, this study introduced novel objectives aimed at maximizing investment-environment-energy-society benefits and solving algorithms, the jellyfish search algorithm (JSA), for calculation. Results showed 10.085% - 11.728% of the annual average benefits in different cases. Ultimately, the optimization integration of flexible energy-use regulations and microgrid operation scheduling yields 15.655% - 18.016% of the annual average benefits in different cases.

关键词： Microgrid operation scheduling optimization Flexible energy-use regulation Occupant-oriented regulation Multi-strategy parallel regulation jellyfish search algorithm

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Optimization and Application of Task Scheduling in Virtual Testing Platforms 1

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8th Symposium on Digital Instrumentation and Control Technologies and Applications for Nuclear Power Plants

作者： Kang, Ning Li, Ming-gang Zhang, Hai-bin Shi, Ye China Techenergy Co Ltd Beijing 100094 Peoples R China

ISBN: (数字)9789819770557

ISBN: (纸本)9789819770571;9789819770557;9789819770540

Virtual testing platforms offer cost-effective and user-friendly solutions for constructing environments for software testing. When utilized for software testing, the platforms face the challenge of efficiently allocating tasks under the constraints of limited resources and testing requirements, aiming to minimize the completion time of tasks. This paper addresses the challenges of resource conflicts, task priorities, task dependencies, and resource occupancy in software testing by developing an automated testing task scheduling model, introducing an improved jellyfish search algorithm (IJSA). This algorithm reasonably arranges the sequence of testing task steps under these constraints to minimize testing time. Integrating partition search and Levy flight strategies enhances its global search capabilities. Compared to other metaheuristic algorithms, simulation experiments demonstrate significant advantages in solution accuracy of this algorithm. Its application in a virtual testing platform proves the algorithm's ability to determine the optimal task scheduling scheme more stably and effectively.

关键词： Virtual test platform jellyfish search algorithm Task scheduling Resource allocation Automated testing

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Combined cubic generalized ball surfaces: Construction and shape optimization using an enhanced JS algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2023年 176卷

作者： Hu, Gang Li, Min Zhong, Jingyu Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

The construction and shape optimization of complex shape-adjustable surfaces is a crucial and intractable technique in Computer Aided geometric Design (CAGD), which has a wide range of application value in many product designs and manufacturing fields involving complex surfaces modeling. In this paper, a novel combined cubic generalized Ball (CCG-Ball, for short) surfaces is constructed and the shape optimization of CCG-Ball surfaces is studied by an enhanced jellyfish search (JS) algorithm. First and foremost, we construct the CCG-Ball surfaces with multiple shape parameters based on a class of cubic generalized Ball basis functions, and then derive the conditions of G1 and G2 continuity for the surfaces. The shapes of CCG-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Secondly, the shape optimization of CCG-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by swarm in-telligence algorithm. In this regard, an enhanced JS termed EJS algorithm, combined with sine and cosine learning factors, local escape operator, opposition-based learning and quasi-opposition learning strategies, is introduced to improve the convergence speed and calculation accuracy of the JS algorithm. Finally, by mini-mizing the energy of CCG-Ball surfaces as the evaluation standard, the shape optimization models of the surfaces with G1 and G2 geometric continuity are established, respectively. The EJS algorithm is utilized to solve the established models, and the CCG-Ball surfaces with minimum energy are obtained. The example results illustrate the ability of EJS algorithm in effectively solving the shape optimization problems of complex CCG-Ball surfaces.

关键词： jellyfish search algorithm Sine and cosine learning factors Local escape operator Opposition-based learning Cubic generalized Ball surfaces Energy minimization

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Composite Improved algorithm Based on jellyfish, Particle Swarm and Genetics for UAV Path Planning in Complex Urban Terrain

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SENSORS 2024年第23期24卷 7679页

作者： Wang, Qi Yi, Wenjun Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

关键词： jellyfish search algorithm particle swarm algorithm genetic algorithm UAV path planning complex urban terrain modelling CEC2005 benchmark functions

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Cost-based analysis and optimization of distributed generations and shunt capacitors incorporated into distribution systems with nonlinear demand modeling

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 198卷 116844-116844页

作者： Eid, Ahmad Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Qassim Univ Coll Engn Dept Elect Engn Unaizah 56452 Saudi Arabia

This paper optimizes the allocations of Distributed Generators (DG) and Shunt Capacitor (SC) banks to minimize a multi-objective problem consisting of four different indices. These indices include active/reactive power loss reduction, voltage profile improvement, and system stability enhancement. Consequently, the costs of energy loss, allocated devices, and total operation are estimated per year for different installed DG/SC pairs for studied systems. Multi-DG and SC banks are optimally installed in the standard 69-bus and 136-bus radial distribution systems considering six nonlinear load models. The six models are generated from the nonlinear relationship between the system voltage and demand. Furthermore, the load type affects the DG/SC allocation optimization problem and other performance parameters such as the total voltage deviation (TVD), stability index (SI), and the power drawn from the utility. A new metaheuristic optimization called the jellyfish search algorithm (JSA) is adopted and applied to allocate the DG units and the SC banks optimally into the distribution systems. The savings in the annual energy loss reach 65.88%, 95.99%, and 97.91% with industrial load demand for 1DG/1SC, 2DG/2SC, and 3DG/3SC allocations, respectively for the 69-bus RDS. While for the 136-bus RDS, the saving reaches 39.71% with constant-impedance demand, 58.84%, and 73.87% with commercial demand for 1DG/1SC, 3DG/3SC, and 6DG/6SC pairs, respectively. It is found that the JSA is practical and suitable for solving these nonlinear optimization problems, and it gives better results as compared to other algorithms in the literature.

关键词： Cost of energy loss Power loss reduction jellyfish search algorithm Multi-objective optimization Total operating cost

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