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Simultaneous optimization of curvature and curvature variation for tool path generation in high-speed milling of corners

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第2期44卷 1-17页

作者： Yang, Xuefeng You, Youpeng Yang, Wen-An Nanjing Univ Aeronaut & Astronaut Natl Key Lab Sci & Technol Helicopter Transmiss Nanjing 210016 Peoples R China

The machining corners are an important feature of aircraft structure components. In the process of high-speed milling of the corner, the discontinuity of tangential direction and curvature of the tool path can lead to frequent starting and stopping of speed, sharp change of cutting force and feed direction, which will result in violent vibration of the machining tool, and ultimately affect the efficiency and quality of part machining. Recently, it has been demonstrated that G(3) (i.e., the variation rate of curvature with continuity) tool path has the potential to improve the smoothness and reduce the mechanical vibration in the field of corner machining. This study presents a corner-looping milling strategy for optimizing the tool path smoothly based on the first generation of tool path. A multi-objective tool path optimization model subject to multiple constraints such as G(3) continuity, transition error and transition length is developed to simultaneously minimize the curvature variation of the tool path curve and the extreme value of curvature. Moreover, an upgraded teaching-learning-based optimization algorithm is used to effectively solve the optimal solution. Compared with that of previously published literature, the proposed method can effectively improve the smoothness and maximum curvature of the corner tool path to avoid the sharp change of curvature. Experiment results show that the surface quality and the machining efficiency are improved.

关键词： Tool path optimization Corner milling G(3) continuity teaching-learning-based optimization Vibration reduction

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A hybrid ITLHHO algorithm for numerical and engineering optimization problems

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第7期37卷 3900-3980页

作者： Kundu, Tanmay Garg, Harish PDPM Indian Inst Informat Technol Math Discipline Design & Mfg Jabalpur Madhya Pradesh India Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

Harris hawks optimization (HHO) is one of the newest metaheuristic algorithms (MHAs) which mimic the interdependent behaviour and hunting style of Harris hawks in nature. It is an efficient swarm optimization technique that has been used to solve various kinds of optimization problems. However, for some optimization cases, it has a tendency to be trapped into local search space and it endures an improper balance between exploitation and exploration. To get rid of this situation and to explore the global searching ability of HHO, an effective hybrid method improved teaching-learning HHO (ITLHHO) has been developed using improved teaching-learning-based optimization for solving different kinds of engineering design and numerical optimization problems. The performance of ITLHHO has been demonstrated by 33 well-known benchmark functions, including IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) and 10 multidisciplinary challenging engineering optimization problems. After illustration, the outcomes of the proposed ITLHHO are compared with several recently developed competitive MHAs. Additionally, the ITLHHO results are statistically investigated with the Wilcoxon rank-sum test and multiple comparison test to show the significance of the results. The experimental results suggest that ITLHHO significantly outperforms other algorithms and becomes a remarkable and promising tool for solving various kinds of optimization problems.

关键词： benchmark functions CEC 2019 engineering optimization harris hawks optimization teaching-learning-based optimization

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A surrogate-assisted hybrid swarm optimization algorithm for high-dimensional computationally expensive problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 72卷

作者： Li, Fan Li, Yingli Cai, Xiwen Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve high-dimensional computationally expensive problems. Two swarms are, respectively, used in different optimization states. The first swarm uses the teaching-learning-based optimization in the early stage to enhance the exploration. The second swarm uses the particle swarm optimization in the later stage to accelerate convergence. Two different pre-screening criteria based on the corresponding evolutionary rules are proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 and an engineering optimization problem are used to validate the efficiency of the proposed algorithm. In addition, a comprehensive analysis is conducted to demonstrate the effectiveness of each main component of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over other compared algorithms.

关键词： Computationally expensive problems Hybrid swarm Particle swarm optimization teaching-learning-based optimization Surrogate

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The Approach of Using a Horizontally Layered Soil Model for Inhomogeneous Soil, by Taking into Account the Deeper Layers of the Soil, and Determining the Model's Parameters Using Evolutionary Methods

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1420-1420页

作者： Jesenik, Marko Trbusic, Mislav Univ Maribor Fac Elect Engn & Comp Sci Koroska Cesta 46 Maribor 2000 Slovakia

Featured Application Larger grounding systems are often designed using the finite element method. A finite element method model can only be made if the data of the soil structure are known in advance. A problem arises when the soil is not homogeneous and the horizontally layered soil model poorly represents the actual conditions in the soil. This paper introduces an approach that describes the use of an analytical horizontally layered soil model for inhomogeneous soil. In this approach, deeper layers of the soil are also considered, which are enabled using higher distances d in Wenner's method, as in the case of simply dividing the area into smaller parts. Our approach is *** A new approach using a horizontally layered analytical soil model for inhomogeneous soil is presented. The presented approach also considers deeper soil layers, which is not the case when simply dividing the area of interest into smaller subareas. The finite element method model was used to prepare test data because, in such a case, the soil parameters are known. Six lines simulating Wenner's method were used, and their results were combined appropriately to determine the soil parameters of nine subareas. To determine the soil parameters in the scope of each subarea, different optimization methods were used and compared to each other. The results were analyzed, and Artificial Bee Colony was selected as the most appropriate method among those tested. Additionally, the convergence of the methods was analyzed, and Memory Assistance is presented, with the aim of shortening the calculation time. In this study, three-, four-, five-, and six-layered soil models were tested, and it is concluded that the three-layered model is most appropriate. A finite element method model based on the soil determination results was constructed to verify the results. The results of the Wenner's method simulation in the cases of the test data and final model were compared to confirm the accuracy of the prese

关键词： grounding system soil model finite element method differential evolution artificial bee colony teaching-learning-based optimization

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Mining periodic patterns from spatio-temporal trajectories using FGO-based artificial neural network optimization model

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NEURAL COMPUTING & APPLICATIONS 2022年第6期34卷 4413-4424页

作者： Upadhyay, Pragati Pandey, M. K. Kohli, Narendra Uttarakhand Tech Univ Dept Comp Sci Dehra Dun Uttarakhand India Amrapali Inst Technol & Sci Comp Sci & Engn Dept Haldwani Uttarakhand India Harcourt Butler Tech Univ Comp Sci & Engn Dept Kanpur 208002 Uttar Pradesh India

Periodic patterns are occurrences that occur regularly over a long period of time at a specific location. In recent years, mining periodic patterns have become a popular area of research. There are several difficulties to map and find the periodical motion pattern of moving objects. Recently, the most challenging task is hidden periodic pattern detection from a historical object movement. To tackle these challenges, we have proposed artificial neural network (ANN) for periodic pattern mining. The proposed methodology involves three major sections namely data pre-processing, clustering, and periodic pattern mining. The two stages involved in data pre-processing are the dataset produced from the obtained matrix and the matrix form that expresses the given sub-sequence. Next, the data clustering for further process is carried out by using teaching-learning-based optimization (TLBO) algorithm. Finally, periodic pattern mining is effectively performed using artificial neural network (ANN)-based football game-based optimization (ANN-FGO) with nine constraints namely item, value, cyclic pattern, aggregate, length, sequence, similarity, duration, and gap constraints, respectively. However, the proposed model outperformed other existing methods in terms of performance.

关键词： Spatiotemporal periodic pattern mining Clustering Object movement tracking teaching-learning-based optimization Artificial neural network

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A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-14页

作者： Zeng, Jun Yao, Juan Gao, Min Wen, Junhao Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China

In the cloud manufacturing process, service composition can combine a single service into a complex service to meet the task requirements. An efficient service composition strategy is crucial, as it affects the efficiency of resource and capacity sharing in the cloud manufacturing system. However, in the face of a large-scale environment, the existing methods have the problems of slow convergence and instability. To solve the problems above, we propose an improved optimization method, named improved-TC. Specifically, we are inspired by the horizontal crossover of CSO in the hybrid-TC teaching phase, the Hybrid-TC is proposed in our previous work, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). Improved-TC is an improvement on the learning phase of hybrid-TC algorithm, we change the search method of hybrid-TC in the learning phase to a one-dimensional search, thereby some dimensions in the population that are trapped in the local optimum have the chance to jump out of the iteration. Experiments show that our proposed method has a faster convergence speed and more stability in the face of service composition in large-scale environments.

关键词： Cloud manufacturing Service composition Quality of service teaching-learning-based optimization The crisscross optimization algorithm

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Parameterization-based trajectory planning for an 8-DOF manipulator with multiple constraints

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Biomimetic Intelligence & Robotics 2025年第1期5卷 67-76页

作者： Ziwu Ren Zhongyuan Wang Xiaohan Liu Rui Lin Robotics and Microsystems Research Center Soochow UniversitySuzhou 215131China

A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint *** such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly *** teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal *** and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the ***,the planned trajectory has no theoretical end-effector deviation for the task *** approach can provide a reference for the motion planning of other redundant manipulators.

关键词： 8-DOF manipulator Trajectory planning Parameterization Obstacle constraints teaching-learning-based optimization

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INNA: An improved neural network algorithm for solving reliability optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第23期34卷 20865-20898页

作者： Kundu, Tanmay Garg, Harish Chandigarh Univ Dept Math Mohali 140413 Punjab India Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching-learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.

关键词： Neural network algorithm teaching-learning-based optimization Constrained optimization Reliability redundancy allocation problem

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Wind turbine electrohydraulic transmission system control for maximum power tracking with pump fault

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2023年第9期237卷 1702-1716页

作者： Kumar, Neeraj Venkaiah, Paladugu Sarkar, Bikash Kumar Maity, Subhendu Natl Inst Technol Meghalaya Dept Mech Engn Shillong India Natl Inst Technol Meghalaya Dept Mech Engn Shillong 793003 India

The wind power generation system plays a significant role in the power sector as it is an environment-friendly green power system, increasing power demand, and technological development in wind power systems. Wind turbine systems are exposed to the harsh environment with continuous variation of wind speed with gusts causing damage and failure in system components along with the fluctuation of generated power. The hydrostatic transmission system has become one of the promising solutions over the gear transmission system for transmitting power from the turbine rotor to the generator. Further to reduce power generation costs in wind power systems, a suitable control system with parametric uncertainty and system fault plays a significant role. In this study, the 5 MW wind turbine model has been developed with the combination of blade element momentum theory and the electrohydraulic transmission system model. Moreover, the wind turbine system model has been imposed fault in the pump of electrohydraulic transmission system. The proposed wind turbine system model has been validated with the existing result. The blade element momentum theory has been used to estimate the optimum pump turbine couple rotational speed for maximum power tracking. Double loop controller has been used for wind turbine power transmission system control. The first controller loop has been used for pump and wind turbine system speed control for maximum power tracking, as a passive fault tolerance controller and the second control loop for motor and generator system speed control to regulate the frequency of the generated power. Interval type 2-fuzzy proportional-integral-derivative controller are suitable for high degree of uncertain system like wind power system due to their footprint of uncertainties. Proper choice of footprint of uncertainty provides robust performance against uncertainties and dynamic performance. Hence, the primary and secondary controller has been developed as interval type 2-

关键词： Electrohydraulic fuzzy type 2 transmission wind turbine teaching-learning-based optimization inertial weight local search

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teaching-learning-based strategy to retrofit neural computing toward pan evaporation analysis

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SMART STRUCTURES AND SYSTEMS 2023年第1期32卷 37-47页

作者： Ikram, Rana Muhammad Adnan Khan, Imran Moayedi, Hossein Foong, Loke Kok Le, Binh Nguyen Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Univ Haripur Dept Econ Haripur Pakistan Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

关键词： environmental management multi-layer perceptron pan evaporation teaching-learning-based optimization

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