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ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Kumar, Harmesh Wadhwa, Amandeep Singh Akhai, Shalom Kaushik, Arishu Punjab State Aeronaut Engn Coll Patiala India Maharishi Markandeshwar Deemed Univ Mullana Ambala 133207 Haryana India Baba Banda Singh Bahadur Engn Coll Fatehgarh Sahib India Univ Inst Engn & Technol Panjab Univ Chandigarh India

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as teaching-learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 mu s) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 mu s) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile indu

关键词： electric discharge machining teaching-learning-based optimization modeling material erosion rate surface roughness

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Optimize Energy of Train Simulation with Track Slope Data 18

Optimize Energy of Train Simulation with Track Slope Data

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Huang, Sy-Ruen Sung, Hung-Kang Ma, Chuan-Hsun Feng Chia Univ Dept Elect Engn Taichung 40724 Taiwan Feng Chia Univ PhD Program Elect & Commun Engn Taichung 40724 Taiwan Feng Chia Univ Masters Program Green Energy Sci & Technol Taichung 40724 Taiwan

ISBN: (纸本)9781467365963

This study aims to establish equations for energy consumptions of Taiwan Railways Administration (TRA) trains in operation considering the variations of slope to determine the traction energy cost (TEC). Where railway transportation has been one of the important means of transport for mankind, since TRA became electrified the parameters for train operations have been able to be accurately recorded. The equations for overall energy consumption and resistance parameters are completed for the overall energy consumption of TRA trains and resistance to them to simulate the operation of the whole train model. The inputs of different conditions of trains and track sections are then analyzed for energy use, as the energy use by train travel is obvious. That by the behavior of energy use, can be divided in three conditions for analysis which is acceleration, coasting and deceleration. In the analysis of the energy in the journey, the variation of its consumption is subject to multiple factors because of the difference in operation model and the variation in the track slope data. In view of decreasing unnecessary energy consumption, the equations for train energy consumption and the track parameters are substituted in the teaching-learning-based optimization (TLBO) technique, which can compute the optimal results fast. The optimal results being incorporated in the trains can help reduce unnecessary energy consumption and errors by human operations and arrive at the destination on schedule. By the equations for energy consumption, train parameters, and TLBO, our study proved to be able to reduce energy use as well as ensure of Top Transport Time Expenditure (TTE) of the trains.

关键词： train simulation teaching-learning-based optimization train energy saving

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Hybrid DE-TLBO Algorithm for Solving Short Term Hydro-thermal Optimal Scheduling with Incommensurable Objectives

Hybrid DE-TLBO Algorithm for Solving Short Term Hydro-therma...

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第三十二届中国控制会议

作者： JIANG Xingwen ZHOU Jianzhong School of Hydropower＆Information Engineering Huazhong University of Science and Technology School of Hydropower ＆ Information Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Digital Valley Science and Technology

ISBN: (纸本)9781479900305

In this paper,a hybrid differential evolution and teaching-learning-based optimization(hDE-TLBO) algorithm is proposed to solve the multi-objective short-term optimal hydro-thermal scheduling(MOSOHTS).In the MOSOHTS model, the total fuel cost and emission effects are optimized as two conflict objectives,the water transport delay between reservoirs and the valve-point effect of thermal units are also taken into *** the proposed method,Differential Evolution (DE) and teaching-learning-based optimization(TLBO) algorithm are combined together;DE plays the role of "Teachers Refresher" in TLBO and enhances the overall efficiency of the *** addition,an effective constraint handling approach is presented to deal with the complex *** effectiveness of the proposed method is tested on three scenarios of a hydro-thermal power system with four cascaded hydro plants and three thermal *** results show that hDE-TLBO is a viable alternative way to generate optimal non-dominated scheduling schemes for the MOSOHTS.

关键词： optimal hydro-thermal scheduling differential evolution teaching-learning-based optimization multi-objective optimization constraint handling approach

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Brain Storming Incorporated teaching-learning-based Algorithm with Application to Electric Power Dispatch

Brain Storming Incorporated Teaching-Learning-Based Algorith...

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3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Ramanand, K. R. Krishnanand, K. R. Panigrahi, Bijaya Ketan Mallick, Manas Kumar MEA Engn Coll Elect & Elect Engn Dept Malappuram India Siksha O Anusandhan Univ Multidisciplinary Res Cell Bhubaneswar Orissa India Indian Inst Technol Dept Elect Engn Delhi India Siksha O Anusandhan Univ Dept Comp Sci & Engn ITER Bhubaneswar Orissa India

ISBN: (纸本)9783642353796;9783642353802

This paper intends to incorporate a brain storming mechanism into the existing teaching-learning-based optimization (TLBO) algorithm. The potential solutions of TLBO evolve using the primitive steps that are maintained between the acts of teaching and learning. Another novel algorithm, Brain Storm optimization (BSO) sticks to the philosophy of interchange of ideas by a team to develop as a whole. The brain storming methods from BSO are introduced into the working of TLBO and applied to a well-studied electric power dispatch problem of high intricacy. The results are compared to best of the existing solutions to demonstrate the efficacy of the proposed hybrid algorithm.

关键词： Brain storm optimization Dynamic economic dispatch Osborn's Rules for Idea Generation teaching-learning-based optimization

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Ambidextrous Socio-Cultural Algorithms 20th

Ambidextrous Socio-Cultural Algorithms

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Lemus-Romani, Jose Crawford, Broderick Soto, Ricardo Astorga, Gino Misra, Sanjay Crawford, Kathleen Foschino, Giancarla Salas-Fernandez, Agustin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Covenant Univ Ota Nigeria Univ Diego Portales Santiago Chile

ISBN: (纸本)9783030588175;9783030588168

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature's behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

关键词： Social-inspired metaheuristics Socio-cultural inspired metaheuristics Human-based algorithm teaching-learning-based optimization Twitter optimization Ambidextrous metaheuristics

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Applying the Modified TLBO Algorithm to Solve the Unit Commitment Problem

Applying the Modified TLBO Algorithm to Solve the Unit Commi...

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World Automation Congress (WAC)

作者： Khazaei, Peyman Dabbaghjamanesh, Morteza Kalantarzadeh, Ali Mousavi, Hasan Shiraz Univ Technol Elect & Comp Engn Dept Shiraz Iran Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA Niroosazeh Co TIS Smart Home Shiraz Iran

ISBN: (纸本)9781889335513

In the day-ahead power systems scheduling, system operators formulate and solve the unit commitment (UC) problem to determine ON/OFF status and power dispatch of the generating units. Although various methods have been presented to solve the UC problem, it is a mixed-integer optimization problem which is hard to find its global optimum solution. In this paper, the new teaching-learning-based optimization (TLBO) technique, which is an evolutionary algorithm, is employed to solve the unit commitment problem. The application of the TLBO on the UC include two phases, teaching and learning phases. A set of population is defined for the UC problem solution. Then, an iterative teaching and learning procedure is implemented to determine the best UC population which has the minimum operating cost while all system and units' constraints, including spinning reserve constraint, are satisfied. Simulation studies show the convergence speed and effeteness of the proposed TLBO algorithm to solve the UC problem. The proposed algorithm is compared with several existing methods.

关键词： Power system operation unit commitment teaching-learning-based optimization evolutionary algorithm

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A Short Study in Formulation of TLBO and its Synergized Applications with Clustering 2

A Short Study in Formulation of TLBO and its Synergized Appl...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Kurada, Ramachandra Rao Kanadam, Karteeka Pavan Tripathi, R. C. SVECW Dept CSE Bhimavaram India RVR & JC Coll Engn Dept IT Guntur India IMS Dept IT Noida India

ISBN: (纸本)9781467369114

Real world problems are also classified to multi-objective optimization problems since they are tailored with more than one objective functions for which the optimization is advantageous simultaneously. The best possible outcome among these objective function is being optimized from the set of solutions rather than finding a single solution. One of the most recently originated teaching-learning-based optimization (TLBO) algorithm in Evolutionary Approaches also addresses such issues. This paper aims to formulate the three transcripts basic, elitist and improved TLBO masterminded by R.V. Rao at a single congregation and investigate their competence and vitality in solving multiple objective functions of clustering techniques when used over real-time datasets. Also, this study annals the contributions made by novel researches in synergizing clustering applications with TLBO.

关键词： teaching-learning-based optimization meta-heuristics evolutionary algorithms multi-objective optimization clustering

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基于TLBO算法的不确定性条件下复杂产品协同设计的可靠性拓扑优化

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Engineering 2023年第3期22卷 71-81页

作者： Zhaoxi Hong Xiangyu Jiang 冯毅雄 Qinyu Tian 谭建荣 State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province Zhejiang UniversityHangzhou 310027China

复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑... 详细信息

复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑优化方法。将由板结构形成的支撑物作为拓扑优化对象,重量轻、稳定性好。将不确定性下的可靠性优化和结构拓扑优化协同处理。首先,利用有限差分法将优化问题中的不确定性参数修正为确定性参数。然后,将不确定性可靠性分析和拓扑优化的复杂嵌套解耦。最后,利用TLBO算法求解解耦模型,该算法参数少,求解速度快。TLBO算法采用了自适应教学因子,在初始阶段实现了更快的收敛速度,并在后期进行了更精细的搜索。本文给出了一个液压机基板结构的数值实例,说明了该方法的有效性。

关键词： Plates structure Reliability Collaborative topology optimization teaching-learning-based optimization algorithm Uncertainty Collaborative design for product life cycle

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A metaheuristic-based optimum tuning approach for tuned liquid dampers for structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第3期31卷 e1907-e1907页

作者： Ocak, Ayla Bekdas, Gebrail Nigdeli, Sinan Melih Istanbul Univ Cerrahpasa Dept Civil Engn Istanbul Turkey

This study focuses on the optimization of tuned liquid dampers (TLDs) by using metaheuristic methods using a natural phenomenon as inspiration. Unlike tuned mass dampers (TMDs), TLDs consist of two parts of bodies that contain the passive liquid that is stationary and the active part of the liquid that involves in sloshing. To optimize the amount of optimum active and passive liquids by finding the best dimensional properties of the liquid tank, a two-degree-of-freedom (DOF) model was used as TLD in the dynamic analysis used in the optimization that aims to reduce the maximum displacement of the structure under earthquake excitations. For this purpose, water, oil, and glycerin liquids were selected for TLD. TLD parameters were optimized with the help of Jaya algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, and teaching-learning-based optimization (TLBO). Total acceleration and maximum displacement were evaluated after optimization. The performance of TLD under earthquake excitations has been also compared with TMDs. According to the results, the optimized TLD has a better performance than TMD, and Jaya algorithm has a slightly better performance than the other algorithms.

关键词： flower pollination algorithm harmony search algorithm Jaya algorithm optimization teaching-learning-based optimization tuned liquid damper (TLD)

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Inertia-weight local-search-based TLBO algorithm for energy management in isolated micro-grids with renewable resources

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷 107877-107877页

作者： Abaeifar, Amin Barati, Hassan Tavakoli, Ali Reza Islamic Azad Univ Dept Elect Engn Dezful Branch Dezful Iran Islamic Azad Univ Dept Elect Engn Ahvaz Branch Ahvaz Iran

Integration of distributed generators and loads in the form of a microgrid enhances the supply reliability and reduces the generation costs and greenhouse gas emissions. Microgrids should be properly managed via an energy management system (EMS) to minimize the overall costs of the system. In this paper, an Inertia-Weight Local-Search based teaching-learning-based optimization (named IWLS-TLBO), is proposed as an exploration-exploitation balanced metaheuristic algorithm to solve the EMS problem. The proposed IWLS-TLBO algorithm is inspired by the principles of human learning based on self-perceptual and self-regulatory abilities of people in facing various problems and adopting the best decision based on the previous experiences. By emphasizing of the IWLS-TLBO algorithm on both exploration (ability to search for new solutions) and exploitation (ability to exploit from existing solutions), the search space can be more effectively investigated than the original TLBO algorithm. In order to justify the performance of the proposed IWLS-TLBO algorithm, obtained results on different bench-mark functions are compared with those of achieved by the original TLBO and other metaheuristics. Then, the IWLS-TLBO is applied to optimize the EMS problem which is formulated based on a unit commitment problem in an isolated microgrid with renewable energy resources. Simulation results demonstrate the superiority of the proposed IWLS-TLBO algorithm against the existing metaheuristic algorithms, in terms of solution quality and convergence speed.

关键词： Microgrids Renewable energy resources Energy management system Unit commitment teaching-learning-based optimization

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