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Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem

PEERJ COMPUTER SCIENCE 2025年 11卷 e2688-e2688页

作者： Li, Qingrui Zhou, Yongquan Luo, Qifang Guangxi Minzu Univ Coll Artificial Intelligence Nanning Guangxi Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Guangxi Peoples R China

Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem;second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge transfer and the selection probability of elite individuals. Based on this decision, the algorithm either transfers elite knowledge from other tasks or updates the current task through self-perturbation. Experimental results indicate that, compared to other advanced MTO algorithms, the proposed algorithm achieves the most accurate solutions on multitask benchmark functions, the five-task and 10-task planar kinematic arm control problems, the multitask robot gripper problem, and the multitask car side-impact design problem.

关键词： Multi-task optimization Snake optimization Multitask snake optimization algorithm Planar kinematic arm control problem intelligence algorithm

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Hierarchical optimization of current-limiting devices with discrete parameters in multi-terminal DC power grids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第1期17卷 219-229页

作者： Mei, Jun Chen, Xiaoyu Zhang, Bingtian Zhang, Sen Lei, Liupeng Guo, Jiawei Southeast Univ Sch Elect Engn Nanjing Peoples R China Shanghai Urban Power Supply Co Shanghai Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

Optimizing current-limiting devices (CLDs) in multi-terminal DC grids is essential to limit the fault current while reducing the cost of devices. However, current studies mainly focus on optimizing CLDs based on continuous parameters in simple DC grids, which cannot meet the optimal efficiency demand of CLDs in complex multi-terminal DC grids and results in each CLD needing to be customized separately, increasing the production cost of devices. Therefore, to improve the optimal efficiency under complex multi-terminal DC grids, the principle of fault area division based on the coupled degree of converter and DC grid and the corresponding hierarchical optimal implementation schemes of CLDs are proposed first. Then, the effects of CLD parameters on the fault current are analysed, and their sensitive ranges of current-limiting are revealed. In combination with the error range of inductance, the principle of discrete parameter design of current-limiting inductance parameters is put forward. Next, the discrete parameters of CLDs are optimized by an intelligence algorithm. Finally, the hierarchical optimal method of CLDs based on discrete parameters is validated on a six-terminal DC grid in PSCAD/EMTDC. The proposed method can satisfy the demand of current-limiting, which is more in line with the standardization requirements in practice.

关键词： multiterminal DC power grids Optimisation techniques fault currents intelligence algorithm optimisation current-limiting devices Protection apparatus CLD hierarchical optimization power grids PSCAD-EMTDC

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intelligence algorithm Research of Convection Diffusion Equation and Numerical Simulation

Intelligence Algorithm Research of Convection Diffusion Equa...

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2014 International Conference on Artificial,intelligence and Software Engineering(AISE 2014)

作者： Ya-mian PENG Ling WANG College of Science Hebei United University

When we study heat conduction process, expansion of gas process and electromagnetic field questions and so on dissemination, we frequently meets the convection proliferation partial differential equation, the numerical solution research of convection diffusion equation is essential. This paper discusses the convection- diffusion equation is the problem with the method for solving inverse problems. This article first discusses the convection- diffusion equation is the finite difference method, using five-point difference scheme for solving them. Then a two-dimensional convection- diffusion equations in the numerical simulation of initial boundary value problem, get a good result. Finally, we study the convection- diffusion equation Inverse Problems of Numerical Solution. Above all, it will be conducted for Laplace transform method for approximate analytical solution of semi-infinitive sand column during dispersive test under the condition of one dimensional steady flow and injection of tracer with constant concentration of order one;the normal distribute function method and the inverse function method of inverse parameter control problem also be introduced. Compare with them, the minimum squared method can drawn the effect of calculation's accuracy about people's action. During the course, it can be worked by computer, and calculation is simple. The high accuracy is its best aspect.

关键词： intelligence algorithm GA Environmental hydraulics Inverse problem

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SARE: an optimized method for detection functional regulatory elements in genome

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EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2025年第1期26卷 1-10页

作者： Hosseinpoor, Mohammadjavad Islamic Azad Univ Dept Comp Engn Estahban Branch Estahban Iran Islamic Azad Univ Estahban Branch Young Researchers & Elite Club Estahban Iran

BackgroundMetaheuristic algorithms have been widely employed to solve optimization problems, but their application in analyzing health-related data remains limited. This is primarily due to the complexity of health data, particularly genomic and epigenetic datasets. In genomics, a critical challenge is identifying genomic factors that collaboratively influence disease-associated genes. Enhancer-promoter interactions play a key role in gene regulation, and accurately predicting these elements is essential for developing tailored therapeutic strategies. Despite advances in computational tools, limitations such as fixed-fragment approaches and computational inefficiency hinder the detection of biologically relevant *** and methodsHis study introduces the simulation annealing regulatory element (SARE) method, a novel approach based on the simulated annealing algorithm, to identify enhancer-promoter-like interactions from Hi-C (chromosome conformation capture) datasets. The SARE method addresses the limitations of traditional fixed-fragment approaches by detecting variable-length regulatory elements. The Hi-C dataset utilized in this research was derived from mouse embryonic stem cells (ESCs) and processed using advanced filtering techniques to retain valid interactions. A sensitivity analysis was performed to optimize critical parameters, such as the cooling schedule and initial configurations, ensuring robust performance across *** proposed method was benchmarked against traditional techniques, including HiCUP and HiC-Pro, using statistical metrics such as precision (0.85), recall (0.78), and F1-score (0.81). SARE demonstrated superior performance, identifying a significantly higher number of interactions with increased biological relevance. Approximately 70% of the detected interactions overlapped with known enhancer-promoter pairs, while the remaining 30% potentially represent novel regulatory mechanisms. Computational efficiency an

关键词： Regulatory elements Chromatin organization Genomic analysis intelligence algorithm Traditional methods (HiCUP and HiC-pro) Simulation annealing regulatory element (SARE)

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Artificial immune algorithm-sparrow search algorithm and its application in network intrusion detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 5001-5011页

作者： Liu, Jianhua Wang, Zhiheng Xian Univ Posts & Telecommun Sch Comp Sci Xian 710000 Shaanxi Peoples R China

In order to solve the problem that the population diversity of sparrow search algorithm (SSA) decreases and easily falls into the local optimal solution when it approaches the global optimal, an artificial immune algorithm-sparrow search algorithm (AIA-SSA) is proposed in this paper by combining artificial immune algorithm and sparrow search algorithm. This paper uses 10 benchmark functions for experimental simulation of AIA-SSA algorithm, and compares it with five widely used intelligent algorithms and SSA. Experimental results show that AIA-SSA overcomes the deficiency of SSA and improves the search accuracy, convergence speed and stability of the algorithm. Meanwhile, this paper applies AIA-SSA to network intrusion detection and constructs a network intrusion detection model based on support vector machine (SVM). After testing, the accuracy of AIA-SSA-SVM prediction for various network attacks has been greatly improved. It not only shows that AIA-SSA-SVM has a broad application prospect in the field of network security, but also verifies the feasibility and advanced nature of AIA-SSA in solving practical engineering problems.

关键词： Intrusion detection SSA intelligence algorithm adaptive search differential evolution algorithm

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Stress prediction of bridges using ANSYS soft and general regression neural network

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STRUCTURES 2022年 40卷 812-823页

作者： Wu, Ying Zhang, Lihua Liu, Hongbing Lu, Pengzhen Jiaxing Nanhu Univ Jiaxing 314001 Zhejiang Peoples R China Zhejiang Univ Technol Coll Civil Engn Hangzhou 320023 Zhejiang Peoples R China

Bridge managers are often eager to identify the carrying capacities and service performance characteristics of bridge structures. In view of the massive number of bridges managed and maintained in China, providing a quick evaluation of bridge performance bridges has become an urgent challenge for bridge engineering. Aiming at many adverse problems of bridge load tests, such as their high costs, long time consumption, low efficiency, impact on traffic, and damage to bridge structures, a new method for bridge static behavior prediction is proposed herein, based on dynamic load test data. The method adopts a generalized regression neural network algorithm with fast convergence and a nonlinear approximation, determines the sensitive parameters based on a full-bridge sensitivity analysis, forms training samples using a uniform design sampling method, and establishes intelligent agent model. The initial finite element analysis model of the bridge structure is updated using the proposed proxy model and dynamic load test data, and the static behavior of the existing bridge structure is accurately predicted based on the updated finite element model. To verify the correctness of the proposed method, a practical bridge is considered as the research object, and the static response of the bridge structure is predicted. The predicted results are consistent with the static load test results for the bridge, verifying the correctness of the proposed method. The results show that the maximum error between the theoretical calculation result and measured value is only 4.34% and the minimum error is only 3.52%. The strain prediction results are consistent with the measured results, and the modified theoretical analysis model provides high accuracy for strain prediction results under different working conditions. The application in practical engineering shows that the prediction results under different load conditions are adaptable and highly accurate.

关键词： Bridge engineering Dynamic load test data intelligence algorithm Regression analysis Static behavior prediction

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Slope Stability Analysis Based on Leader Dolphins Herd algorithm and Simplified Bishop Method

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IEEE ACCESS 2021年 9卷 28251-28259页

作者： Ma, Li Zhao, Jianqiang Zhang, Jianguo Xiao, Shuangshuang Xian Univ Sci & Technol Sch Energy Engn Xian 710054 Peoples R China China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Univ Technol Sch Math & Stat Xuzhou 221018 Jiangsu Peoples R China

According to the principles of bionics and predatory behavior of dolphins, Leader Dolphin Herd algorithm (LDHA) was designed, whose performance is also tested on the basis of heuristic algorithm, showing that LDHA is obviously superior to the other algorithms in calculation accuracy and efficiency. The minimum safety factor of sliding surface was taken as the objective function, the recurrence formula of safety factor based on simplified Bishop Method was regarded as constraint condition, a non-linear multi-objective optimization mathematical model used for analyzing slope stability was established. the process of searching slope sliding surface and solving safety factor were designed on account of LDHA. By taking homogeneous slope and layered slope as calculation examples compared to other intelligent algorithms, the results show that the calculation result of LDHA is close to that of Genetic algorithm and Simulated Annealing algorithm and improved Genetic algorithm. Moreover, the LDHA has less iterations and faster convergence rate, and the result is closer to the optimal solution, which indicates the feasibility and practicability of LDHA in solving the minimum stability safety factor.

关键词： Dolphins Optimization Safety Heuristic algorithms Surface treatment Simulated annealing Genetic algorithms Leader Dolphin Herd algorithm slope stability analysis model Simplified Bishop Method intelligence algorithm

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Modelling method of data-driven model combined with a priori knowledge and its application in average particle size estimation of composite colloidal sols

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2021年第2期99卷 558-570页

作者： Zhou, Yang Li, Shaojun East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

A universally applicable hybrid modelling method is proposed for nonlinear industrial processes that combine the a priori process knowledge with a data-driven model. This method constructs a unified framework for the modelling process by integrating a data-driven modelling technique, sampling detection technique, constraint optimization problem, and an evolutionary algorithm. In the modelling process, a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori knowledge. By adding the constraints of process a priori knowledge, more information can be obtained about the actual process and the over-fitting problem can be avoided to some extent, especially when modelling a system with a small quantity of samples. In order to show the effectiveness of the method proposed in this paper, two general data-driven models, the polynomial regression model and radial basis function network model, are used as case studies. Moreover, a function simulation experiment is designed to test effectiveness, and applied to estimate average particle size of ZrO2-TiO2 composite colloidal sols.

关键词： constraint handling data-driven modelling hybrid model intelligence algorithm prior knowledge

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A Survey of intelligence Optimization algorithm with Thermodynamics

A Survey of Intelligence Optimization Algorithm with Thermod...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Xu, Xing Hu, Na Ying, Weiqin Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9783038350125

There are many similarities between the intelligence algorithms and the Statistical Physics and Thermodynamics in many aspects, such as the research object, tasks and methods. Firstly, this paper presents the hybrid intelligence algorithms improved by Thermodynamics. Then the theory analysis of intelligence algorithms by Thermodynamics is presented. Finally, a new research direction, that is the novel intelligence algorithm based on statistical physics and thermodynamics, is proposed for the future.

关键词： intelligence algorithm Thermodynamics Statistical Physics Optimization

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Impact of the Pandemic on Online Grocery Delivery:Taking Instacart Company as an Example

Impact of the Pandemic on Online Grocery Delivery:Taking Ins...

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作者： Jiaxuan Tang Syracuse University

The Pandemic has had an unprecedented impact on online grocery shopping;the worldwide lockdown has made online grocery shopping a rigid demand for citizens. During the global pandemic, many online grocery shopping companies grew up rapidly to the enormous demand. Especially Instacart, the most developed company in online grocery shopping,gained huge success in late 2019. This article analyzes Instacart's strengths, such as the three algorithms, its weaknesses, and how it can grow sustainably after Covid-19.

关键词： online grocery shopping Instacart Covid-19 intelligence algorithm

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