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Techno-economic analysis of a grid-connected PV/battery system using the teaching-learning-based optimization algorithm

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SOLAR ENERGY 2020年 203卷 69-82页

作者： Ashtiani, Mina Najafi Toopshekan, Ashkan Astaraei, Fatemeh Razi Yousefi, Hossein Maleki, Akbar Tamin Ehtiajat Fanni Tehran TAF Co Tehran Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehn Diode Idial Factoran Tehran Iran Shahrood Univ Technol Fac Mech Engn Shahrood Iran

In this paper, a novel framework for optimal sizing of a grid-connected photovoltaic (PV)/battery system is presented to minimize the total net present cost using a novel optimization algorithm based on the teaching and learning process, namely teaching-learning-based optimization (TLBO). The TLBO algorithm is an efficient optimization method based on the teacher's influence on the learners' output in a class. This article shows how backup PV/battery systems can reduce electricity bills, even in countries where their electricity is cheap and subsidized. In comparison to the non-renewable case, the net present cost (NPC) and the cost of energy (COE) of the on-grid PV/battery system are 15.6% and 16.8% more efficient, respectively. The NPC and COE factors were calculated and compared with two other popular optimization algorithms, particle swarm optimization, and genetic algorithm to validate the proposed approach and to ascertain the strength and accuracy of the TLBO algorithm. To compare the results, different cities were examined and the similarity of the results showed that the system is efficient regardless of the surrounding climate. Also, sensitivity analyses on different cities' climatic data, different load demands and PV prices outlined the economic optimal size of the system.

关键词： Sustainable energy Sizing PV-battery system Peak demand teaching-learning-based optimization

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An ameliorated teaching-learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur's entropy and Otsu's between class variance

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INFORMATION SCIENCES 2020年 533卷 72-107页

作者： Wu, Bo Zhou, Jianxin Ji, Xiaoyuan Yin, Yajun Shen, Xu Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

In this paper, multi-threshold image segmentation approaches using an improved teaching-learning-based optimization algorithm (DI-TLBO) are presented and the proposed DI-TLBO-based methods obtain satisfactory segmentation results. This work is presented as follows. Firstly, two random numbers are introduced to determine the learning methods of the learner in the teacher phases and the learner phases of DI-TLBO. Randomness of the learning methods further improves global optimization ability of DI-TLBO. Self-feedback learning phase and mutation-crossover phase are also introduced into DI-TLBO algorithm, which makes DI-TLBO achieve better exploration ability. The comparative results of DI-TLBO with other evolutionary algorithms (EAs) on a set of benchmarks functions demonstrate that DI-TLBO acquires better solution accuracy than other EAs. Then the proposed DI-TLBO algorithm is applied to solve multi-level threshold image segmentation problems modeled by Otsu's between class variance function and Kapur's entropy function. Experiments comparing DI-TLBO-based methods with other EAs based approaches on standard test images show that DI-TLBO-based methods possess superior performance in terms of both solution accuracy and stability of segmentation results. Finally, the proposed DI-TLBO-based methods are successfully applied in casting X-ray image segmentation for multi-level threshold. Although the defects in high resolution X-ray image (3072 x 2400) are easy to be ignored and omitted when being detected artificially, all the defects are segmented perfectly using the proposed DI-TLBO-based methods. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Multilevel thresholding Image segmentation teaching-learning-based optimization Otsu's threshold Kapur's entropy

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A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第2期27卷 1523-1533页

作者： Sevinc, Ender Dokeroglu, Tansel Turkish Aeronaut Assoc Univ Fac Engn Dept Comp Engn Ankara Turkey TED Univ Fac Engn Dept Comp Engn Ankara Turkey

Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.

关键词： teaching-learning-based optimization extreme learning machines metaheuristic classification feature selection

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Constrained optimization based on improved teaching-learning-based optimization algorithm

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INFORMATION SCIENCES 2016年 352卷 61-78页

作者： Yu, Kunjie Wang, Xin Wang, Zhenlei E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

This paper proposes an improved constrained teaching-learning-based optimization (ICTLBO) method to efficiently solve constrained optimization problems (COPs). In the teacher phase of ICTLBO, the population is partitioned into several subpopulations, and the direction information between the mean position of each subpopulation and the best position of population guide the corresponding subpopulation to the promising region promptly. Information exchange between different subpopulations is used to discourage premature convergence of each subpopulation. Furthermore, in the learner phase, a new learning strategy is introduced to improve the population diversity and enhance the global search ability. Three different constraint handling methods are adopted for three situations, which are infeasible, semi-feasible, and feasible situations, during the evolution process. To evaluate the performance of ICTLBO, 22 benchmark functions presented in CEC2006 and 18 benchmark functions introduced in CEC2010 are chosen as the test suite. Moreover, four widely used engineering design problems are selected to test the performance of ICTLBO for real-world problems. Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms. (C) 2016 Elsevier Inc. All rights ***

关键词： teaching-learning-based optimization Constrained optimization Constraint handling learning strategy

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Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

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COMPUTERS & CHEMICAL ENGINEERING 2017年 99卷 314-324页

作者： Yu, Kunjie While, Lyndon Reynolds, Mark Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Univ Western Australia Sch Comp Sci & Software Engn Nedlands WA 6009 Australia Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ethylene cracking furnace Cyclic scheduling teaching-learning-based optimization

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An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time

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NEUROCOMPUTING 2015年 148卷 260-268页

作者： Xu, Ye Wang, Ling Wang, Sheng-yao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 10084 Peoples R China

In this paper, an effective teaching-learning-based optimization algorithm (TLBO) is proposed to solve the flexible job-shop problem with fuzzy processing time (FJSPF). First, a special encoding scheme is used to represent solutions, and a decoding method is employed to transfer a solution to a feasible schedule in the fuzzy sense. Second, a bi-phase crossover scheme based on the teaching-learning mechanism and special local search operators are incorporated into the search framework of the TLBO to balance the exploration and exploitation capabilities. Moreover, the influence of the key parameters on the TLBO is investigated using the Taguchi method. Finally, numerical results based on some benchmark instances and the comparisons with some existing algorithms are provided. The comparative results demonstrate the effectiveness and efficiency of the proposed TLBO algorithm in solving the FJSPF. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Flexible job-shop scheduling problem Fuzzy processing time teaching-learning-based optimization Taguchi method

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Modified Energy-Efficient Range-Free Localization Using teaching-learning-based optimization for Wireless Sensor Networks

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IETE JOURNAL OF RESEARCH 2018年第1期64卷 124-138页

作者： Sharma, Gaurav Kumar, Ashok Natl Inst Technol Dept Elect & Commun Engn Hamirpur Himachal Prades India

This paper presents an energy-efficient Modified Distance Vector Hop algorithm using teaching-learning-based optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.

关键词： DV-Hop algorithm Localization optimization teaching-learning-based optimization Wireless sensor networks

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Stacking Sequence optimization for Maximum Fundamental Frequency of Simply Supported Antisymmetric Laminated Composite Plates using teaching-learning-based optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2017年第6期21卷 2281-2288页

作者： Topal, Umut Dede, Tayfun Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Technol Dept Civil Engn TR-61830 Trabzon Turkey Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

This paper proposed a technique based on teaching-learning-based optimization (TLBO) for fundamental frequency optimization of simply supported antisymmetric laminated composite plates. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. The first order shear deformation theory is used to calculate the natural frequencies of laminates. In order to perform the optimization based on the TLBO, a special code is written in MATLAB software environment. Several numerical examples are presented to show the efficiency of the proposed algorithm. Also, the optimization problems are solved using other optimization algorithm such as Artifical Bee Colony (ABC) algorithm and the results are compared. It is concluded that TLBO algorithm can effectively be used for fundamental frequency optimization of antisymmetric laminated plates. The CPU time and the number of function evaluations required by TLBO are much smaller than those of the other algorithms tested. This indicates that TLBO is a competitive optimization algorithm for this class of problems.

关键词： laminated composite plates teaching-learning-based optimization fundamental frequency optimization

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A multi-objective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint

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JOURNAL OF CLEANER PRODUCTION 2015年第Aug.15期101卷 337-347页

作者： Lin, Wenwen Yu, D. Y. Zhang, Chaoyong Liu, Xun Zhang, Sanqiang Tian, Yuhui Liu, Shengqiang Xie, Zhanpeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Univ Michigan Dept Mech Engn SMWu Mfg Res Ctr Ann Arbor MI 48109 USA Shan Dong Hoteam Software Co Ltd Jinan Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan Peoples R China

Industry is responsible for nearly half of the global energy consumption. Recent studies on sustainable manufacturing focused on energy saving to reduce the unit production cost and environmental impacts. Besides energy consumption, certain manufacturing activities in machine shops, such as the use of cutting fluids, disposal of worn tools, and material consumption, also cause other environmental impacts. Since all these activities lead to carbon footprint directly or indirectly, carbon footprint can be employed as a new and overall environment criterion in manufacturing. In this study, an integrated model for processing parameter optimization and flow-shop scheduling was developed. Objectives to minimize both makespan and carbon footprint were considered simultaneously, which was solved by a multi-objective teaching learning-based optimization algorithm. Furthermore, three carbon-footprint-reduction strategies were employed to optimize the scheduling results: (i) postponing strategy, (ii) setup strategy, and (iii) processing parameter preliminary optimization strategy. In the theoretical aspect, the strategies greatly improved the performance of the optimization results through reducing machine idle time and cutting down the search space. From the perspective of practical applications, these strategies greatly help elevate production efficiency and reduce environmental impacts. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Processing parameter optimization Flow-shop scheduling teaching-learning-based optimization Carbon footprint Sustainable manufacturing

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A two-stage personalized recommendation based on multi-objective teaching-learning-based optimization with decomposition

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NEUROCOMPUTING 2021年 452卷 716-727页

作者： Zou, Feng Chen, Debao Xu, Qingzheng Jiang, Ziqi Kang, Jiahui Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China

Due to its successful application in information filtering and knowledge retrieval systems in the era of big data, personalized recommender system plays a significant role in meeting personalized demands of people from big data and has become a hot research hotspot. Traditional recommender systems only guarantee the recommendation accuracy and the recommendation diversity will be lost. In this paper, we propose a two-stage personalized recommendation (TSPR) algorithm based on an improved collaborative filtering (ICF) and multi-objective teaching-learning-based optimization (MOTLBO/D) with decomposition. Firstly, ICF made use of each user' preference and social neighborhood information to obtain a candidate recommendation list for each target user. In the proposed ICF method, the jump relationship between users and their unrated items was mined to describe the characteristic behavior information of users more fully. Moreover, MOTLBO/D was adopted to simultaneously optimize accuracy and diversity of recommendation list of items for each target user. In the proposed MOTLBO/D algorithm, the learner representation strategy is designed based on attributes of the recommendation problem, and the learners are updated by an improved TLBO procedure. Finally, the simulation results on two Movielens datasets show that TSPR is effective and efficient in personalized recommender systems. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Two-stage Personalized recommendation teaching-learning-based optimization Multi-objective optimization Decomposition

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