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Performance analysis and optimization of a 20 MWh piston hydraulic gravity energy storage (PHGES) system

ENERGY 2025年 323卷

作者： He, Denghui Shen, Yangfei Luo, Nan Xian Univ Technol State Key Lab Water Engn Ecol & Environm Arid Area Xian 710048 Peoples R China Xian Univ Technol Inst Water Resources & Hydroelect Engn Dept Power Engn Xian 710048 Peoples R China

The volatility and intermittency of renewable energy sources, such as wind and solar power, significantly affect energy supply stability. Consequently, the analysis and design of large-capacity energy storage systems have emerged as a crucial research area. This paper conducted a parameter analysis and optimization design of a large-capacity piston hydraulic gravity energy storage (PHGES) system employing MATLAB/Simulink numerical simulation. Initially, following the modular modeling concept, three subsystem modules were developed, encompassing the gravity well mathematical model, piston movement mathematical model and valve operation mathematical model. The reliability and validity of the simulation models were validated through experimental results. Furthermore, the investigation delved into the system dynamic characteristics, energy conversion performance, and economic viability under design conditions. Subsequently, employing parameter sensitivity analysis and correlation analysis methods, the study elucidated the influence trends and degrees of six key system design parameters-gravity well height, gravity well diameter, piston height, piston density, return pipe crosssectional area, and valve opening area-on energy conversion performance and economic feasibility within the studied parameter ranges. Lastly, based on the parameter study outcomes, the primary impacting factors of the system were identified. Through the utilization of linear weighting and differential evolution algorithm, the optimal design parameters for a 20 MWh PHGES system were determined. The optimized system efficiency reached 76.67 %, reflecting a 0.27 % improvement over the initial design conditions. Economically, the levelized cost of energy (LCOE) decreased from 0.6921 CNY/kWh to 0.6258 CNY/kWh, signifying a 9.6 % reduction compared to the original system. The study provides a theoretical foundation and technical guidance for the utilization of large-capacity PHGES technology.

关键词： Gravity energy storage Piston hydraulic system Optimization design differential evolution algorithm Levelized cost of energy

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Multiobjective Optimization in Logic Synthesis Based on TB-RM Dual Logic

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第4期44卷 1380-1393页

作者： Zhou, Yuhao Wang, Zhen Kong, Xiangxue Pan, Hongyang He, Zhenxue Zhang, Ying Jiang, Jianhui Xiao, Limin Wang, Xiang Tongji Univ Sch Comp Sci & Technol Shanghai 201804 Peoples R China Shanghai Univ Elect Power Sch Comp Sci & Technol Shanghai 200090 Peoples R China 40 Middle Sch Linyi Math Dept Middle Sch Linyi 276017 Peoples R China Fudan Univ Sch Microelect State Key Lab Integrated Chips & Syst Shanghai 200433 Peoples R China Hebei Agr Univ Key Lab Agr Big Data Hebei Prov Baoding 071001 Peoples R China Sanda Univ Sch Informat Sci & Technol Shanghai 201209 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Traditional logic synthesis methods are based on Boolean logic, which tends to produce redundant logic structures in dense circuit applications, such as complex number operations and error detection/correction coding. Traditional Boolean and Reed-Muller (TB-RM) logic synthesis method combining traditional Boolean (TB) logic and Reed-Muller (RM) logic can improve comprehensive optimization indexes and reduce cost. The existing TB design method is not effective when dealing with constrained systems with high-resource utilization requirements. In addition, traditional synthesis methods do not consider multiobjective optimization of area, power consumption and reliability. To solve these problems, we propose an effective dual logic synthesis method (EDSM), which includes dual logic detection method (DDM) and differential evolution algorithm based on multidimensional mutation strategy (DE-MMS). DDM can complete the logic detection function, and DE-MMS can further optimize the polarity. In addition, to evaluate the soft errors occurring more efficiently at the logic level, we propose a soft error rate (SER) estimation model. Experimental results show that compared with state-of-the-art evolutionary algorithms, EDSM can search for optimal solutions in all optimization problems;compared with commonly used TB-based minimization methods, EDSM has obvious advantages in multiobjective optimization of area, power consumption and SER. After 6-LUT FPGA technology and standard cells mapping, by selecting area as the optimal cost implementation, we obtain average improvements in the area of 14% and 2%, respectively.

关键词： Logic Optimization Power demand Reliability Integrated circuit reliability Circuit faults Logic functions Logic circuits Input variables Logic gates differential evolution algorithm logic detection logic synthesis method multiobjective optimization soft error rate (SER)

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Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation

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CATENA 2021年 198卷 105024-105024页

作者： Mohammadi, Babak Guan, Yiqing Moazenzadeh, Roozbeh Safari, Mir Jafar Sadegh Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Shahrood Univ Technol Fac Agr Dept Water Engn Shahrood Iran Yasar Univ Dept Civil Engn Izmir Turkey

River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ***(-1), MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ***(-1), MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.

关键词： differential evolution algorithm Hybrid technique Mahabad river Multi-layer perceptron Particle swarm optimization Suspended sediment load

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A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

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SOFT COMPUTING 2013年第12期17卷 2293-2309页

作者： Kong, Xiangyong Ouyang, Haibin Piao, Xiaoxue Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

In this paper, a new adaptive grouping differential evolution (AGDE) algorithm is proposed to improve the optimization performance by implementing a prediction strategy of the constraints for constrained optimization problems. It is unnecessary to calculate the constraint values when dealing with the constraints in this method. The constraints are handled after a simple prediction according to the Lipschitz condition. When the constraints are very complex, the load arisen from the calculation of the constraint values is reduced dramatically and the feasibility of the solutions remains with great probability. In AGDE algorithm, the population is dynamically grouped to three subpopulations with respective newly-designed mutation strategy. Meanwhile, the mutation factor and crossover probability are adopted associated with the evolutionary process according to the information of the entire population. Both of the above improvements not only increase the diversity of population and speed up the convergence, but also reduce the complexity of the parameter selection. Four sets of comparative experiments are carried out to evaluate the feasibility and effectiveness of the proposed method that deals with the constraints.

关键词： Constrained optimization Prediction of the constraint values differential evolution algorithm Self-adaptive Grouping

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Optimizing hybrid deep learning models for drug-target interaction prediction: A comparative analysis of evolutionary algorithms

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EXPERT SYSTEMS 2025年第2期42卷

作者： Sharma, Moolchand Bhatia, Aryan Dutta, Ashit Kumar Alsubai, Shtwai GGSIPU Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Diriyah Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Coll Comp Engn & Sci Al Kharj Al Kharj Saudi Arabia

In the realm of Drug-Target Interaction (DTI) prediction, this research investigates and contrasts the efficacy of diverse evolutionary algorithms in fine-tuning a sophisticated hybrid deep learning model. Recognizing the critical role of DTI in drug discovery and repositioning, we tackle the challenges of binary classification by reframing the problem as a regression task. Our focus lies on the Convolution Self-Attention Network with Attention-based bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), a hybrid model combining convolutional neural network (CNN) blocks, self-attention mechanisms, and bidirectional LSTM layers. To optimize this complex model, we employ differential evolution (DE), Particle Swarm Optimization (PSO), Memetic Particle Swarm Optimization algorithm (MPSOA), Fire Hawk Optimization (FHO), and Artificial Hummingbird algorithm (AHA). Through thorough comparative analysis, we evaluate the performance of these evolutionary algorithms in enhancing the CSAN-BiLSTM-Att model's effectiveness. By examining the strengths and weaknesses of each algorithm, our study aims to provide valuable insights into DTI prediction, identifying the most effective evolutionary algorithm for hyperparameter tuning in advanced deep learning models. Notably, Fire-hawk optimization (FHO) emerges as particularly promising, achieving the highest Concordance Index (C-index) as 0.974 for KIBA datasets and 0.894 for DAVIS datasets and demonstrating exceptional accuracy in ranking continuous predictions across both the datasets.

关键词： bidirectional LSTM binding affinity convolution neural network convolution self-attention network DAVIS & KIBA datasets differential evolution algorithm drug-target interaction particle swamp optimization

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Dimensional multi-objective optimization design for 2RPU-RPS parallel mechanism

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ROBOTICA 2025年第3期43卷 1087-1109页

作者： Peng, Siyang Du, Li Che, Linxian Jin, Zujin Chongqing Technol & Business Univ Sch Mech Engn Chongqing Peoples R China Chongqing Vocat Inst Engn Sch Intelligence Mfg & Traff Chongqing Peoples R China Suzhou Univ Sch Mech & Elect Engn Suzhou Peoples R China

A modeling method of multi-objective optimization design for parallel mechanisms (PMs) is proposed, whose implementation is illustrated with 2RPU-RPS mechanism as an example. The orientation of biased output axis on moving platform is depicted by spherical attitude angles, and its kinematic model is deduced through vector method. With screw theory as mathematic tool, a comprehensive evaluation method of kinematic performance for PM is established. On this basis, the expensive constrained multi-objective optimization model of dimensional parameters for the discussed mechanism is constructed. The NSDE-II algorithm, formed by replacing the genetic algorithm operators in non-dominated sorting genetic algorithm II (NSGA-II) with DE operators, is utilized to solve this multi-objective optimization problem, thus obtaining multiple Pareto optimal solutions with engineering application significance, which proves the feasibility and effectiveness of the proposed modeling method and algorithm. Moreover, the normalization coverage space and the minimum adjacent vector angle are proposed to evaluate the computational performance of NSDE-II. Finally, the potential engineering application value for the optimized 2RPU-RPS PM is presented.

关键词： parallel mechanism motion/force transmission performance regular transmission orientation workspace multi-objective optimization differential evolution algorithm

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THE OPTIMAL DEDICATION OF ORDERING POLICY TO GROUP B ITEMS WITHIN ABC ANALYSIS: THREE TUNEFULLY LEADING METAHEURISTICS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第2期21卷 853-877页

作者： Moharramian, Ali Pasandideh, Seyed Hamid Reza Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

This paper presents a mixed integer nonlinear programming model to determine the optimal ordering policy for group B items in ABC analysis. We model a multi-product inventory control system using different policies, including a continuous review policy, a periodic review policy, and a base stock policy. Also, limitations on the shortage, service level, and storage space have been considered in the model to match the actual world conditions. Three meta-heuristic algorithms named differential evolution, swarm robotics, and imperialist competitive were used to find a solution that minimizes the system's total cost. The results showed that the differential evolution works better than other algorithms. In addition, the base stock policy is the optimal policy for ordering group B items.

关键词： ABC analysis base stock policy fixed order size policy fixed order interval policy swarm robotics algorithm differential evolution algorithm imperialist competitive algorithm

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Optimization strategy for batch-stochastic configuration network models and their application in component content prediction

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 150卷

作者： Lu, Rongxiu Hu, Xingrong Pei, Cong Yang, Hui Dai, Wenhao Zhu, Jianyong East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China Key Lab Adv Control & Optimizat Jiangxi Prov Nanchang 330013 Peoples R China

This study addresses the challenges of inefficiency and uncertain quality in incrementally adding hidden layer nodes to a stochastic configuration network (SCN). An optimized approach for batch-wise stochastic configuration network (BSCN) using an enhanced differential evolution (DE)algorithm is introduced. Initially, the inequality constraints of SCN is studied to analyze and establish the correlation between objective parameters and network residuals. Subsequently, to speed the network's training velocity, a BSCN is utilized for developing the regression model. Combining the DE algorithm with regional contraction and greedy selection strategies. Specifically it leverages the robust global search prowess of the standard DE while mitigating its limitations in local search and ensuring global convergence. The formulation of this enhanced DE, termed regional contraction and greedy selection differential evolution (SGDE), is elaborated in detail, and an analysis and validation of its global convergence are conducted. Comparative experiments with the conventional DE underscore the superior optimization efficacy of SGDE. The applicability of SGDE enhanced BSCN (SGDEBSCN) is corroborated through six real-world regression tasks. These tasks demonstrate that SGDE-BSCN not only excels in configuring hidden layer nodes but also exhibits enhanced error minimization and superior generalization capabilities with an equivalent number of hidden layer nodes. Additionally, a practical case study focused on predicting component content in rare earth extraction processes validates the effectiveness of the proposed method. The empirical results show that the application of SGDE-BSCN to artificial intelligence (AI) and artificial intelligence in the field of rare earth extraction shows high prediction accuracy and fast processing speed.

关键词： Stochastic configuration network differential evolution algorithm Rare earth extraction Implemented artificial intelligence Application of artificial intelligence

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Traceability of surface water pollution based on the SSO+DE algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2025年 114卷 112-122页

作者： Jia, Dongyan Zhao, Liqiang Song, Jinling Guo, Dongliang Liu, Xiaoqing Hebei Normal Univ Sci & Technol Hebei Innovat Ctr Smart Percept & Appl Technol Agr Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China

River water pollution incidents seriously threaten the ecological environment and people's health. Effective determination of the pollution source information contributes to minimizing the scope of water pollution incidents. A surface water pollution traceability model based on the social spider optimization algorithm and differential evolution algorithm was proposed to more accurately trace the pollution sources of surface water pollution events. The diffusion law of pollutants in rivers could be described by the migration and diffusion model. Relevant parameters (e.g., water flow velocity and river diffusion coefficient) varied depending on the season and weather. First, parameters in the migration and diffusion model were calibrated using the social spider optimization algorithm to coincide with the real hydrological environment. Then, the traceability of pollution source information was completed by simulating mutation, crossing, and selection between individuals based on a differential evolution algorithm, which obtained the optimal pollution source location, discharge volume, and discharge time. Finally, the relevant experiment data of the water pollution events between the Jingshi Section and the Puyang Section of the Waterproof River in Ref. [32] were used for simulation experiments. Results were compared with the probabilistic method and coupled density method in Ref. [32]. The average absolute errors of the two methods were 201.0 and 36.43, respectively. The error of the traceability results obtained by the traceability model in the work significantly decreased, with an average absolute error of 1.557. The SSO and DE algorithms had good convergence speed and global search capabilities. Besides, the DE algorithm is a multi-objective optimization algorithm solving multi-dimensional spatial data. Therefore, the surface water pollution traceability model had a high traceability accuracy and certain application value. It reduced the impact of water pollution on

关键词： Water pollution traceability Migration and diffusion model Parameter calibration Social spider optimization algorithm differential evolution 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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