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

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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

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teaching-learning-based optimization algorithm in Dynamic Environments

Teaching-Learning-Based Optimization Algorithm in Dynamic En...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Zou, Feng Wang, Lei Hei, Xinhong Jiang, Qiaoyong Yang, Dongdong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

ISBN: (纸本)9783319037530;9783319037523

In many real word problems, optimization problems are nonstationary and dynamic. optimization of these dynamic optimization problems requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. In this paper, for the first time, a multi-swarm teaching-learning-based optimization algorithm (MTLBO) is proposed for optimization in dynamic environment. In this method, all learners are divided up into several subswarms so as to track multiple peaks in the fitness landscape. Each learner is learning from the teacher and the mean of his or her corresponding subswarm instead of the teacher and the mean of the class in teaching phase, and then learners learn from interaction between themselves in their corresponding subswarm in learning phase. Moreover, all subswarms are regrouped periodically so that the information exchange is made with all the learners in the class to achieve proper exploration ability. The proposed MTLBO algorithm is evaluated on moving peaks benchmark problem in dynamic environments. The experimental results show the proper accuracy and convergence rate for the proposed approach in comparison with other well-known approaches.

关键词： teaching-learning-based optimization algorithm Dynamic Environments Multi-swarm Moving Peaks Benchmark problem

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Effective Hybrid teaching-learning-based optimization algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

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Chinese Journal of Mechanical Engineering 2015年第5期28卷 1067-1079页

作者： TANG Qiuhua LI Zixiang ZHANG Liping FLOUDAS C A CAO Xiaojun Industrial Engineering Department Wuhan University of Science and Technology Texas A&M Energy Institute Texas A&M University College Station Technique Center of Dongfeng Peugeot Citroen Automobile Company

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

关键词： two-sided assembly line balancing teaching-learning-based optimization algorithm variable neighborhood search positional constraints zoning constraints synchronism constraints

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

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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

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Optimal dynamic design of planar mechanisms using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2016年第19期230卷 3442-3456页

作者： Chaudhary, Kailash Chaudhary, Himanshu Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India

A two-stage optimization method for optimal dynamic design of planar mechanisms is presented in this paper. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of mechanism links using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e. cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using evolutionary optimization algorithm - "teaching-learning-based optimization (TLBO) algorithm''. The computational performance of TLBO algorithm is compared with another evolutionary optimization algorithm, i.e. genetic algorithm.

关键词： Dynamic balancing equimomental system dynamic design planar mechanism teaching-learning-based optimization algorithm

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Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching-learning-based optimization algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第8期29卷 1715-1737页

作者： Rao, R. Venkata Rai, Dhiraj P. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India Univ Maribor Maribor Slovenia

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting teaching-learning-based optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.

关键词： Sustainable machining processes Micro-machining Parameter optimization teaching-learning-based optimization algorithm A posteriori approach

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

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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

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

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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

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Design optimization of robot grippers using teaching-learning-based optimization algorithm

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ADVANCED ROBOTICS 2015年第6期29卷 431-447页

作者： Rao, R. Venkata Waghmare, Gajanan SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm to obtain the optimum geometrical dimensions of a robot gripper. Design of robot grippers is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirement. Robot grippers are the end effectors used to grasp and hold the objects. A gripper acts as the bridge between the robot arm and the environment around it. Five objectives, namely the difference between the maximum and minimum gripping forces, the force transmission ratio, the shift transmission ratio, length of all the elements of the gripper, and the effort of the gripper mechanism are considered as objective functions. The problem has seven design variables, including six variables as dimensions of the gripper and one variable as the angle between two links of the gripper. Three robot grippers are optimized. Computational results shows that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.

关键词： teaching-learning-based optimization algorithm design optimization robot gripper

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Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm

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SCIENTIA IRANICA 2013年第3期20卷 967-974页

作者： Rao, R. Venkata Kalyankar, V. D. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

The primary objective in multi-pass turning operations is to produce products with low cost and high quality, with a lower number of cuts. Parameter optimization plays an important role in achieving this goal. Process parameter optimization in a multi-pass turning operation usually involves the optimal selection of cutting speed, feed rate, depth of cut and number of passes. In this work, the parameter optimization of a multi-pass turning operation is carried out using a recently developed advanced optimization algorithm, named, the teaching-learning-based optimization algorithm. Two different examples are considered that have been attempted previously by various researchers using different optimization techniques, such as simulated annealing, the genetic algorithm, the ant colony algorithm, and particle swarm optimization, etc. The first example is a multi-objective problem and the second example is a single objective multi-constrained problem with 20 constraints. The teaching-learning-based optimization algorithm has proved its effectiveness over other algorithms. (C) 2013 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Multi-pass turning process Parameter optimization teaching-learning-based optimization algorithm

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