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A constraint relaxation-based intelligence algorithm for workshop layouts with automated guided vehicle systems

JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING 2025年第2期10卷 225-244页

作者： Liu, Ran Wang, Chengkai Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

With the widespread adoption of automated guided vehicle (AGV) systems for material handling in manufacturing plants, it has become practical and crucial to delve into the layout problem associated with AGV systems. In this work, we focus on a unique layout problem encountered in a hybrid workshop where AGV systems are employed for transporting semiproducts along the manufacturing line. Several distinctive features in this system contribute to the challenge of the problem. Notably, manufacturing occurs in an uncertain environment, and certain manufacturing cells may produce semiproducts that do not meet quality standards, necessitating repair. Additionally, each AGV requires recharging in a designated area within the workshop. Given that the proposed layout problem is NP-hard, we present an intelligence variable neighborhood search heuristic integrated with a constraint relaxation strategy to address its complexity. The numerical results demonstrate the algorithm's ability to generate high-quality solutions within a reasonable timeframe, even for large-scale test instances. The layout solutions obtained through our algorithm outperform those produced by the CPLEX solver and the practical layouts devised by the company. This highlights the efficacy of our approach in tackling the unique challenges posed by the layout problem in a hybrid workshop with an AGV system. (c) 2025 China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Facility layout Automated guided vehicle intelligence algorithm Constraint relaxation

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intelligence algorithm for optimization design of the low wind speed airfoil for wind turbine

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 S8119-S8129页

作者： Pang, Xiaoping Wang, Haoyu Chen, Jin Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu Sichuan Peoples R China

In order to develop wind resources in low wind speed (LWS) area, a new intelligence algorithm based on the airfoil profile expressed by B-spline for LWS airfoil is proposed. Considering the design requirements for LWS wind turbine airfoil design and taking the DU airfoil as original design coefficients, the new LWS airfoil families with the thickness of 18, 21, 25, 30, 35, 40% were obtained by the particle swarm optimization based on the improved inertia factor and mode. The results show that, compared with the original DU airfoils, all the LWS airfoil families have better aerodynamic performance under free and fixed transition. Performance of the 18% thickness airfoil is improved most significantly: Under fixed transition condition, the maximum lift coefficient increases by 13.53%, and the maximum lift to drag ratio increases by 10.77%;under the free transition condition, the maximum lift coefficient increases by 18.84%, and the maximum lift to drag ratio increases by 11.92%. The aerodynamic performance of a new airfoil named CQUL-180, taken as an example, was analyzed and validated by the computational fluid dynamics compared with DU96-W-180 airfoil, which verifies the reliability of the intelligence algorithm.

关键词： Low wind speed airfoil intelligence algorithm Particle swarm optimization Aerodynamic performance

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Simulation for Path Planning of OUC-II Glider with intelligence algorithm 10th

Simulation for Path Planning of OUC-II Glider with Intellige...

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10th International Conference on Intelligent Robotics and Applications (ICIRA

作者： Liu, Yuhai Luan, Xin Song, Dalei Su, Zhiqiang Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao Shandong Peoples R China Ocean Univ China Coll Engn 238 Songling Rd Qingdao Shandong Peoples R China

ISBN: (纸本)9783319652894;9783319652887

Addressing the need for exploration of benthic zones utilising autonomous underwater gliders, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater glider OUC-II Glider in near-bottom ocean environment. Near-bottom ocean current data from the College of Oceanic and Atmospheric Science, Ocean University of China have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater glider in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the OUC-II glider and it may be extended for use in any standard autonomous underwater glider.

关键词： OUC-II glider Path planning Simulation intelligence algorithm

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A new intelligent algorithm for solving generalized Nash equilibrium problem

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ALEXANDRIA ENGINEERING JOURNAL 2025年 123卷 17-28页

作者： Wang, Kai Jia, Wensheng Guizhou Univ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Guizhou Prov Key Lab Games Decis Making & Control Guiyang 550025 Guizhou Peoples R China

In this paper, the chaotic whale slime mould algorithm (CWSMA) is designed to solve the generalized Nash equilibrium problem (GNEP). First, the GNEP is converted into a non-linear equation system problem using the Karush-Kuhn-Tucker (KKT) condition and the "min" function. Compared to the classical approach, the transformation process does not require the functions to be quadratically differentiable and strongly convex. The CWSMA is proposed by introducing tent mapping, levy flight strategy and the idea of whale optimization algorithm into the slime mould algorithm (SMA), which has the advantages of higher population diversity, faster convergence, less chance of falling into local optimums, and does not depend on the choice of initial points. Furthermore, the convergence analysis of the CWSMA is given by using Markov processes. Finally, several numerical simulation experiments show that the CWSMA is superior to the SMA, the coevolutionary immune quantum particle swarm optimization algorithm, the projection algorithm and the descent algorithm under certain conditions. The CWSMA not only solves the GNEP effectively, but also has better population diversity, global convergence, and off-line performance.

关键词： Generalized Nash equilibrium Non-linear equation system problem intelligence algorithm Markov process

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A clustering scheduling strategy for space debris tracking

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 157卷

作者： Yao, Jiali Xu, Bo Li, Xin Yang, Shihai Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen Peoples R China

The number of spacecraft and space debris, particularly in low Earth orbit, is rapidly escalating, heightening the demand for rational and effective satellite allocation strategies to alleviate the strain on satellite monitoring and tracking systems. In this paper, a clustering scheduling strategy is proposed for satellites equipped with sensors tasked with monitoring a substantial amount of space debris. To address the challenge of excessive space debris, a dynamic and static clustering division scheme is introduced, leveraging target characteristics in conjunction with the nearest neighbor clustering algorithm. Furthermore, to enhance scheduling efficiency, four performance indicators are proposed to guide the generation of a resource allocation scheme using Genetic algorithm and Simulated Annealing. Simulation results demonstrate that the clustering scheduling strategy effectively clusters a large number of space debris, while the scheduling scheme demonstrates better balance, comprehensiveness, and efficiency. The Genetic algorithm and Simulated Annealing exhibit superior performance compared to their counterparts across various orbital inclinations and altitudes. Notably, in a specific simulation scenario related to constellations, the Genetic algorithm and Simulated Annealing achieve a performance enhancement of 13.48% compared to the Genetic algorithm and an improvement of 8.73% compared to Simulated Annealing.

关键词： intelligence algorithm Multiple satellites scheduling Scheduling strategy Space debris observation Target clustering

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Parameter Identification of a Soil Constitutive Model Based on a Hybrid Genetic Differential Evolution algorithm

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BUILDINGS 2024年第11期14卷

作者： Long, Lin Li, Yunyu Yang, Peiling Tang, Bo Hunan Inst Technol Coll Civil & Construct Engn Hengyang 421002 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China 1 High Sch Shaodong Hunan Hunan 422800 Shaodong Peoples R China Zhongda Intelligent Technol Co Ltd Changsha 410005 Peoples R China

Aiming to address the problem of selecting the parameters of a soil constitutive model in the calculation of foundation pit stability, this paper proposes a hybrid genetic differential evolution algorithm (GADE) which performs by "jumping out of local optima" with "fast convergence" based on the hybrid optimization algorithm strategy and compares the advantages and disadvantages of genetic algorithms (GAs) and differential evolution algorithms (DEs). Three typical test functions were used to evaluate the search efficiency and convergence speed of GAs, DEs, and GADE, respectively. It was found that GADE has the fastest convergence speed and can search for the global optimal solution to the problem, which highlights its excellent optimization performance. At the same time, taking the Shimao Binjiang deep foundation pit as an example, GADE was used to invert the soil modulus parameters of a CX1 measuring point and construct a finite-element model for calculation. The results showed that the simulated calculation curve and the measured displacement curve were in good agreement and the curve fitting reached 95.05%, indicating the applicability and feasibility of applying GADE to identify soil parameters.

关键词： differential evolution genetic algorithm intelligence algorithm constitutive parameter soil creep

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An improved radioactive tracer response analysis and injection profile quantification method based on intelligent algorithms

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JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY 2024年第3期333卷 1039-1053页

作者： Yang, Guofeng Peng, Wenzheng Liu, Dongming Wu, Xiaolong Chen, Meng Liu, Xiangjun Southwest Petr Univ Sch Geosci & Technol Chengdu 610500 Sichuan Peoples R China Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Sichuan Peoples R China China Natl Logging Corp Xian 710200 Sichuan Peoples R China China Oilfield Serv Ltd Langfang 065201 Hebei Peoples R China

An improved radioactive tracer response analysis and interpretation method is proposed. The characteristics of the tracer peaks under different measurement conditions are studied using numerical simulation. An optimization method is designed to solve the tracer's transit time passing a certain distance based on ant colony optimization and Nelder-Mead algorithm. The interpretation model is established to calculate the flow velocities using the time-depth relationship. The application example showed that the proposed method could clearly analyze the downhole flow situations and quantify the injection profile.

关键词： Radioactive tracer logging Injection profile Response analysis intelligence algorithm

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Grey Wolf Optimization algorithm with random local optimal regulation and first-element dominance

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EGYPTIAN INFORMATICS JOURNAL 2024年 27卷 1-14页

作者： Xuan, Yanzhuang Xuan, Shibin Guangxi Minzu Univ Sch Artificial Intelligence Guangxi Key Lab Hybrid Computat & IC Design Anal Daxue east Rd 188 Naning 530006 Guangxi Peoples R China

Due to the classical Grey Wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first -element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.

关键词： intelligence algorithm Grey Wolf algorithm First-element dominance Random local optimization Random mixed population

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Establishment of critical non-depositing velocity prediction model for sediment in drip irrigation laterals based on PSO-SVM

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JOURNAL OF CLEANER PRODUCTION 2024年 457卷

作者： Wang, Chang Bai, Dan Li, Yibo Zhang, Qian Ma, Xin Tian, Donglin Shan, Mingming Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian 710048 Peoples R China Water Resources Res Inst Inner Mongolia Hohhot 010051 Peoples R China

Accurately determining the critical non-depositing velocity for sediment (CNDVS) in drip irrigation laterals is essential to address issues of sediment deposition and clogging in drip irrigation pipes caused by the use of muddy water. This paper focuses on three main factors: the percentage of intermediate-sized sediment particles (P), pipe diameter (D), and sediment concentration (S), all of which significantly influence the CNDVS in drip irrigation laterals. To predict the CNDVS, a comprehensive gradient test was developed and the Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was applied to establish a response surface for predicting the CNDVS. Additionally, the PSO algorithm optimized the penalty parameter (C), loss parameter (epsilon), and kernel parameter (delta) in the SVM, resulting in a prediction model for the CNDVS. The proposed model was trained and tested using experimental data, and its prediction performance and accuracy were compared with the regression fitting model. The results indicate that within a specific range, the CNDVS increases with higher values of the three factors. The PSO-SVM model, with optimal parameters combination values of C = 486.37, epsilon = 0.04, and delta = 68.49, provided the most prediction accurate. Compared with the regression fitting model, the PSO-SVM model has the smaller values of Mean Absolute Error (MAE) = 0.0049, Root Mean Square Error (RMSE) = 0.0057, and Percent Bias (PBIAS) = 0.0004, and the larger value of Nash-Sutcliffe Efficiency (NSE) = 0.9874, Willmott index (WI) = 0.9401, and Coefficient of Determination (R2) = 0.9875. These results demonstrate that the PSO-SVM model established in this study exhibits high prediction performance and accuracy. The findings of this research can serve as a foundation for developing optimal operating and flushing flow velocity within the laterals in muddy water drip irrigation systems.

关键词： Utilization of muddy water Sediment deposition intelligence algorithm Intermediate -sized sediment particles Pipe diameter Sediment concentration

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Research on multi-objective optimization method in the design of high-power nanocrystalline alloy high-frequency transformer

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2024年 606卷

作者： Zhang, Pengning Li, Pengyang Zhu, Hailong Li, Bofan Wang, Ning Li, Wei Li, Lei China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China State Grid Corp China North Elect Power Dispatch & Control Sub Ctr Beijing 100053 Peoples R China

The optimization design of high-frequency transformer (HFT) is a multi-objective optimization problem owing to the mutual constraints between design parameters such as power density, efficiency and temperature rise, and traditional design methods often face difficulties in balancing and selecting multiple design parameters. Therefore, this paper derives the structural parameters of HFT, calculates the magnetic core losses and winding losses considering temperature effects, and establishes a 7-node thermal network model. Selecting power density and efficiency as optimization objectives, the HFT is optimized based on multi-objective particle swarm optimization algorithm and non-dominated genetic algorithm, and compared with traditional free parameter scanning method to analyze the advantages of intelligent optimization algorithm. Further, the influence of temperature rise calculation methods on the optimization design results of HFT is compared and analyzed. Finally, a nanocrystalline HFT with 17 MW/m3 power density and 99 % efficiency and an optimization scheme of HFT with 26 MW/m3 power density and 99.2 % efficiency are designed based on the proposed optimization design processes. The effectiveness of the calculation model and optimization method is verified through experimental measurement, and the essential principle of improving the HFT performance through optimization methods is explored through finite element simulation, providing theoretical reference and data support for the optimization design of nanocrystalline HFT.

关键词： High-frequency transformer Multi-objective optimization Optimization method intelligence algorithm Temperature rise calculation

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