检索结果-内蒙古大学图书馆

teaching-learning-based optimization algorithm for Path Planning and Task Allocation in Multi-robot Plant Inspection System

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8999-9021页

作者： Majumder, Arindam Majumder, Abhishek Bhaumik, Rahul Natl Inst Technol Agartala Mech Engn Dept Jirania 799046 Tripura W India Tripura Univ Dept Comp Sci & Engn Agartala 799022 India

A proper planning of path along with task management makes a multi-robot system quicker in task completion and fuel efficient. However, the previous related literature has explored a little bit in optimization of completion time and fuel consumption simultaneously. In this study, a new A* algorithm integrated multi-objective teaching-learning-based optimization (MOTLBO) algorithm is presented for path and task management of multiple robots in plant inspection system. Two objectives namely total completion time (TCT) and total fuel consumption (TFC) are considered for minimization in this study. The developed technique implements A* algorithm for robot route planning and TLBO algorithm for task allocation to the robots. Two mutation operators namely insertion operator and interchange operator are applied to update solutions in teacher phase and learner phase of discrete TLBO. An approach for sequencing of tasks allocated to the robots and a scheme to make the robots' movement collision free is also integrated with the proposed algorithm. For investigating the suitability, an instance of tank firm inspection is solved using this algorithm. Later on, the efficiency of the algorithm is tested by comparing it with the two existing multi-objective optimization algorithms namely Non-dominated Sorting Genetic algorithm II (NSGA-II) and Heuristic Coupled Particle Swarm optimization (HPSO) algorithm. Comparison has also been made between two variants of single objective genetic algorithm with A* algorithm and the proposed technique. The results reveal that the proposed algorithm outperforms the existing algorithms.

关键词： Multi-robot plant inspection system A* algorithm teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Strengthened teaching-learning-based optimization algorithm for numerical optimization tasks

引用

EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1463-1480页

作者： Chen, Xuefen Ye, Chunming Zhang, Yang Zhao, Lingwei Guo, Jing Ma, Kun Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching-learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO's exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1-f7) and six multimodal tasks (f8-f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).

关键词： Metaheuristic optimization algorithm teaching-learning-based optimization algorithm teaching factor Elite system Cauchy mutation

来源：评论

学校读者我要写书评

暂无评论

A teaching-learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem

引用

ENERGY SYSTEMS-optimization MODELING SIMULATION AND ECONOMIC ASPECTS 2024年第4期15卷 1429-1456页

作者： Trachanatzi, Dimitra Rigakis, Manousos Marinaki, Magdalene Marinakis, Yannis Tech Univ Crete Sch Prod Engn & Management Univ Campus Khania Crete Greece

The present research proposes a new Vehicle Routing Problem (VRP) variant, the Environmental Prize-Collecting Vehicle Routing Problem (E-PCVRP). According to the original PCVRP formulation, the scope of the problem is to maximize the total collected prize from the visited nodes and simultaneously minimize the fixed vehicle-utilization cost and the variable cost. In the E-PCVRP formulation, the variable cost is not solely expressed as a vehicle-covered distance but as a load-distance function for CO2 emissions minimization. The teaching-learning-based optimization (TLBO) algorithm is selected as the solution approach. However, TLBO is designed to address continuous optimization problems, while the solution of the E-PCVRP requires a discrete-numbered representation. Thus, a heuristic encoding/decoding technique is proposed to map the solution in a continuous domain, i.e., the Cartesian space, and transform it back to the original form after applying the learning mechanisms, utilizing the Euclidean Distance. The encoding/decoding process is denoted as CRE, and it has been incorporated into the standard TLBO algorithmic scheme, and as such, the proposed TLBO-CRE algorithmic solution approach emerges. The effectiveness of the TLBO-CRE is demonstrated over computational experiments and statistical analysis in comparison to the performance of other bio-inspired algorithms and a mathematical solver.

关键词： teaching-learning-based optimization algorithm Prize-collecting vehicle routing problem Environmental vehicle routing problem Carbon emissions minimization

来源：评论

学校读者我要写书评

暂无评论

Path synthesis of a four-bar linkage using a teaching-learning-based optimization algorithm

引用

INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS 2023年第1期24卷 40-51页

作者： Waghmare, Gajanan G. Rao, R., V Kulkarni, Prafulla C. Gokhale Educ Soc Dept Mech Engn RH Sapat Coll Engn Management Studies & Res Nashi Nasik Maharashtra India SV Natl Inst Technol Dept Mech Engn Surat Gujarat India

This paper presents the performance of a teaching-learning-based optimization (TLBO) algorithm and its elite version named as an elitist teaching-learning-based optimization algorithm (ETLBO) to obtain the optimum set of design parameters for the path synthesis of a four-bar linkage. The minimization of the position error is considered as an objective function and four case studies are considered to verify the efficiency and accuracy of the TLBO and ETLBO algorithms. The synthesized mechanism is obtained for four case studies using the 2 D movable sketch method of SolidWorks. The results have shown that the performance of the TLBO and ETLBO algorithms are better or competitive to the other optimization methods considered by the previous researchers.

关键词： Four-bar linkage path generation coupler curve position error teaching-learning-based optimization algorithm Elitist teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Monitor system and Gaussian perturbation teaching-learning-based optimization algorithm for continuous optimization problems

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第2期13卷 705-720页

作者： Shih, Po-Chou Zhang, Yang Zhou, Xizhao Chaoyang Univ Technol Dept Ind Engn & Management Taichung 413310 Taiwan Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

In this paper, an improved teaching optimization algorithm called monitor system and Gaussian perturbation (GP) teaching-learning-based optimization algorithm (MG-TLBO) is proposed based on several modified variants of TLBO. TLBO is simply divided into two phases: "Teacher phase" and "Learner phase." To further improve the solution accuracy and efficiency, we introduce two mechanisms in the learner phase, namely, monitor system and self-regulated learning (SRL) theory. In the learner phase, we assume that the monitor is the most outstanding individual in the population and possesses self-learning ability to expand his or her own strengths. In addition, GP is deployed to model the SRL process. Therefore, three different versions of MG-TLBO are proposed and related experiments are carried out. The results show that all three MG-TLBOs are more effective than the original TLBO. Finally, comparison of the experimental results with other representative meta-heuristics confirms the validity of the new MG-TLBO. In particularly, the MG-TLBO exhibits an overwhelming advantage over the TLBO, which indicates that the MG-TLBO well balances the exploration and exploitation behavior. All the aforementioned evidence manifests that the MG-TLBO improves the accuracy and efficiency of the solution of the original TLBO.

关键词： Metaheuristic Swarm intelligence teaching-learning-based optimization algorithm Monitor system Gaussian perturbation

来源：评论

学校读者我要写书评

暂无评论

optimization of cylinder liner macro-scale surface texturing in marine diesel engines based on teaching-learning-based optimization algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2021年第2期235卷 329-342页

作者： Mohamad, Salaheldin A. Kamel, Mohamed A. Mil Tech Coll Dept Ship Bldg & Submarine Engn Cairo 11766 Egypt Mil Tech Coll Dept Mech Engn Cairo Egypt

This paper investigates the effect of optimum macro-scale cylinder liners oil groove on the tribological behavior of large bore marine diesel engines. Parabolic bottom shape grooves are selected as the cylinder liner surface texturing. The grooves have been distributed along the stroke in the form of array of circumferential cells with the axial groove centered in each cell. teaching-learning-based optimization algorithm is applied to get the optimum dimensions of oil grooves, where the objective is to minimize the cyclic average total friction force between the top compression piston ring and the cylinder liner. Numerical simulation based on Reynolds equation is presented to study the effect of optimum grooves' dimensions on tribological parameters such as hydrodynamic friction, asperity contact pressure, and hydrodynamic oil film pressure. Results showed that the optimum dimensions oil grooves have a significant effect on the total friction force and the cavitation pressure of the oil film.

关键词： Friction texturation modeling in tribology cylinder liner oil grooves teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Advances in teaching-learning-based optimization algorithm: A comprehensive survey(ICIC2022)

引用

NEUROCOMPUTING 2023年 561卷

作者： Zhou, Guo Zhou, Yongquan Deng, Wu Yin, Shihong Zhang, Yunhui China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing 102249 Peoples R China Guangxi Univ Nationalities Coll Articial Intelligence Nanning 530006 Peoples R China Gunagxi Univ Nationalities Xiangsihu Coll Nanning 532100 Guangxi Peoples R China Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

teaching-learning-based optimization (TLBO) algorithm which imitates the teaching-learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of teaching-learning-based optimization is divided into two phases and each phase executes iterative learning operation. In this paper, we present a comprehensive survey on the recent advances in TLBO. A review of the current literature reveals intriguing challenges and suggests potential future research directions.

关键词： Metaheuristic optimization optimization problem Population-based teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimizing boiler combustion parameters based on evolution teaching- learning-based optimization algorithm for reducing NOR emission concentration

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第11期20卷 20317-20344页

作者： Ma, Yunpeng Liu, Shilin Gao, Shan Xu, Chenheng Guo, Wenbo Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Econ Tianjin Peoples R China

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.

关键词： optimization model extreme learning machine teaching-learning-based optimization algorithm evolution computation boiler combustion optimization

来源：评论

学校读者我要写书评

暂无评论

A reformative teaching-learning-based optimization algorithm for solving numerical and engineering design optimization problems

引用

SOFT COMPUTING 2020年第20期24卷 15889-15906页

作者： Li, Zhuang Zhang, Xiaotong Qin, Jingyan He, Jie Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Univ Sci & Technol Beijing Beijing Key Lab Knowledge Engn Mat Sci Beijing Peoples R China

teaching-learning-based optimization (TLBO) algorithm, which simulates the process of teaching-learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great performance in solving optimization problems. However, it has an inherent origin bias in teacher phase and may fall into local optima for solving complex high-dimensional optimization problems. Therefore, an improved teaching method is proposed to eliminate the bias of converging toward the origin and enhance the ability of exploration during the convergence process. And a self-learning phase is presented to maintain the ability of exploration after convergence. Besides, a mutation phase is introduced to provide a good mixing ability among the population, preventing premature convergence. As a result, a reformative TLBO (RTLBO) algorithm with three modifications, an improved teaching method, a self-learning phase and a mutation phase, is proposed to significantly improve the performance of the TLBO algorithm. Ten unconstrained benchmark functions and three constrained engineering design problems are employed to evaluate the performance of the RTLBO algorithm. The results of the experiments show that the RTLBO algorithm is of excellent performance and better than, or at least comparable to, other available optimization algorithms in literature.

关键词： Evolutionary algorithm Swarm intelligence based algorithm Unconstrained numerical optimization Constrained engineering optimization teaching-learning-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

"Solving discounted {0-1} knapsack problems by a discrete hybrid teaching-learning-based optimization algorithm"

引用

APPLIED INTELLIGENCE 2020年第6期50卷 1872-1888页

作者： Wu, Congcong Zhao, Jianli Feng, Yanhong Lee, Malrey China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China Hebei GEO Univ Coll Informat Engn Shijiazhuang 050031 Hebei Peoples R China ChonBuk Natl Univ Ctr Adv Image & Informat Technol Sch Elect & Informat Engn Jeonju 54896 Chonbuk South Korea

The discounted {0-1} knapsack problem (D{0-1}KP) is a kind of knapsack problem with group structure and discount relationships among items. It is more challenging than the classical 0-1 knapsack problem. A more effective hybrid algorithm, the discrete hybrid teaching-learning-based optimization algorithm (HTLBO), is proposed to solve D{0-1}KP in this paper. HTLBO is based on the framework of the teaching-learning-based optimization (TLBO) algorithm. A two-tuple consisting of a quaternary vector and a real vector is used to represent an individual in HTLBO and that allows TLBO to effectively solve discrete optimization problems. We enhanced the optimization ability of HTLBO from three aspects. The learning strategy in the Learner phase is modified to extend the exploration capability of HTLBO. Inspired by the human learning process, self-learning factors are incorporated into the Teacher and Learner phases, which balances the exploitation and exploration of the algorithm. Two types of crossover operators are designed to enhance the global search capability of HTLBO. Finally, we conducted extensive experiments on eight sets of 80 instances using our proposed approach. The experiment results show that the new algorithm has higher accuracy and better stability than do previous methods. Overall, HTLBO is an excellent approach for solving the D{0-1}KP.

关键词： Discounted {0-1} knapsack problem teaching-learning-based optimization algorithm Self-learning Crossover operator

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：