检索结果-内蒙古大学图书馆

modified teaching-learning-based optimization for Combined Heat and Power Economic Dispatch

INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2017年第5期18卷 1-22页

作者： Pattanaik, Jagat Kishore Basu, Mousumi Dash, Deba Prasad Jadavpur Univ Power Engn Dept Sect 3 Kolkata 700098 W Bengal India Orissa Engn Coll Elect Engn Dept Bhubaneswar Orissa India

This paper develops and suggests modified teaching-learning-based optimization to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. The valve-point loading and forbidden working regions of conventional thermal generator are taken into account. modified teaching-learning-based optimization (MTLBO) introduces Gaussian random variables in the 'Teacher phase' and 'Learner phase' which perks up search efficiency and assurances a high probability of acquiring the global optimum without considerably impairing the convergence speed and the simplicity of the structure of teaching-learning-based optimization (TLBO). The efficacy of the suggested MTLBO technique has been confirmed on three test systems and 15 benchmark functions. Test results of the suggested technique have been matched up to those achieved by other evolutionary techniques. It has been observed from the comparison that the suggested MTLBO technique has the ability to endow with superior solution.

关键词： modified teaching-learning-based optimization teaching-learning-based optimization combined heat and power economic dispatch cogeneration forbidden working region valve-point loading

来源：评论

学校读者我要写书评

暂无评论

A modified teaching-learning-based optimization algorithm for numerical function optimization

引用

INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2019年第6期10卷 1357-1371页

作者： Niu, Peifeng Ma, Yunpeng Yan, Shanshan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Hydropower Stn Adm Taolinkou Reservoir Qinhuangdao 066004 Hebei Peoples R China

In this paper, a kind of modified teaching-learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.

关键词： teaching-learning-based optimization modified teaching-learning-based optimization Exploratory and exploitative capabilities Unconstrained numerical functions CEC2017

来源：评论

学校读者我要写书评

暂无评论

A modified teaching-learning-based optimization algorithm for solving optimization problem

引用

KNOWLEDGE-based SYSTEMS 2021年 212卷 106599-106599页

作者： Ma, Yunpeng Zhang, Xinxin Song, Jiancai Chen, Lei Tianjin Univ Commerce Sch Informat Engn CO-300134 Tianjin Peoples R China

In order to reduce the NOx emissions concentration of a circulation fluidized bed boiler, a modified teaching-learning-based optimization algorithm (MTLBO) is proposed, which introduces a new population group mechanism into the conventional teaching-learning based optimization algorithm. The MTLBO still has two phases: teaching phase and learning phase. In teaching phase, all students are divided into two groups based on the mean marks of the class, the two groups present different solution updating strategies, separately. In learning phase, all students are divided into two groups again, where the first group includes the top half of the students and the second group contains the remaining students. The two groups also have different solution updating strategies. Performance of the proposed MTLBO algorithm is evaluated by 14 unconstrained numerical functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed. In addition, the tuned extreme learning machine by MTLBO is applied to establish the NOx emission model. based on the established model, the MTLBO is used to optimize the operation conditions of a 330 MW circulation fluidized bed boiler for reducing the NOx emissions concentration. Experimental results reveal that the MTLBO is an effective tool for reducing the NOx emissions concentration. (C) 2020 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization modified teaching-learning-based optimization Extreme learning machine NOx emission model Circulation fluidized bed boiler

来源：评论

学校读者我要写书评

暂无评论

A modified teaching-learning optimization algorithm for structural damage detection using a novel damage index based on modal flexibility and strain energy under environmental variations

引用

ENGINEERING WITH COMPUTERS 2022年

作者： Ahmadi-Nedushan, Behrouz Fathnejat, Hamed Yazd Univ Dept Civil Engn Yazd Iran

In this paper, a novel two-stage structural damage detection method using modal flexibility and strain energy-based index (MFSEBI) and modified teaching-learning-based optimization (MTLBO) algorithm is proposed. In the first stage, a novel damage index (MFSEBI) is proposed based on the combination of two structural modal properties including modal strain energy of elements and diagonal members of the structural flexibility matrix to identify the suspected damaged elements. The performance of this indicator is compared to the performance of an indicator derived from the modal strain energy. As the change in the structural modal properties is also affected by environmental variations, in this study, the effect of varying environmental conditions on the performance of the MFSEBI is also examined. In the second stage, the modal response of the structure is used and subsequently updated by MTLBO to estimate the damage extents of the suspected elements. At this stage, the MTLBO algorithm is proposed by enhancing the teaching and learning quality of the TLBO algorithm using new updating mechanisms of each learner position. The performance of the MTLBO and TLBO algorithms is compared in terms of the accuracy of the results and convergence rate. The results of three numerical examples indicate a better performance of the MFSEBI index in damage localization using a smaller number of mode shapes. These results also demonstrate the higher accuracy of the estimated damage extents and the faster convergence rate of the objective function using MTLBO, even with the consideration of measurement noise effects.

关键词： modified teaching-learning-based optimization Two-stage damage detection method Damage index Flexibility matrix Modal strain energy Environmental variations

来源：评论

学校读者我要写书评

暂无评论

leShort-term scheduling of thermal power systems using hybrid gradient based modified teaching-learning optimizer with black hole algorithm

引用

ELECTRIC POWER SYSTEMS RESEARCH 2014年 108卷 16-34页

作者： Azizipanah-Abarghooee, Rasoul Niknam, Taher Bavafa, Farhad Zare, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran

Nowadays, a challengeable subject facing the electric power system operator is how to manage optimally the power generating units over a scheduling horizon of one day considering all of the practical equality, inequality and dynamic constraints. These constraints are comprised of load plus transmission losses balance, valve-point effects, prohibited operating zones, multi-fuel options, line flow constraints, operating reserve and minimum on/off time. In this regard, the proposed framework first presents a practical formulation for the short-term thermal generation scheduling (STGS). It has high-dimensional, high-constraints, non-convex,.non-smooth and non-linear nature and needs an efficient algorithm to be solved. Then, a new optimization approach, known as gradient-based modified teaching-learning-based optimization combined with black hole (MTLBO-BH) algorithm, has been proposed to seek the optimum operational cost. Despite the superior characteristics of the MTLBO and BH algorithms, both of them suffer from the problem of entrapping in local optima. Consequently, the powerful combination of MTLBO and BH and a novel self-adaptive wavelet mutation operator for the organization of the new robust algorithm are proposed in this work. Although the MTLBO-BH algorithm has many properties, some problems still remain to be solved pleasantly. One of these problems is the produced further robust solution that the classical gradient-based technique can overcome it. Finally, performance of the suggested technique is tested on different thermal power systems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Black hole algorithm Gradient-based method modified teaching-learning-based optimization Prohibited operating zones Short-term thermal generation scheduling Valve-point effect

来源：评论

学校读者我要写书评

暂无评论

A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第8期25卷 1577-1588页

作者： Niknam, Taher Azizipanah-Abarghooee, Rasoul Narimani, Mohammad Rasoul Shiraz Univ Technol Dept Elect & Elect Shiraz Iran

This paper proposes a new multi-objective optimization algorithm based on modified teaching-learning-based optimization (MTLBO) algorithm in order to solve the optimal location of automatic voltage regulators (AVRs) in distribution systems at presence of distributed generators (DGs). The objective functions including energy generation costs, electrical energy losses and the voltage deviation are considered in this paper. In the proposed MTLBO algorithm, teacher and learner phases are modified. The considered objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm uses an external repository to save founded Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by compromising) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as genetic algorithm (GA), particle swarm optimization (PSO) and TLBO is extraordinary. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： modified teaching-learning-based optimization Multi-objective Automatic voltage regulator Pareto method

来源：评论

学校读者我要写书评

暂无评论

A new multi objective optimization approach in distribution systems

引用

optimization LETTERS 2014年第1期8卷 181-199页

作者： Hoseini, Mojtaba Hosseinpour, Hossein Bastaee, Bijan Lorestan Univ Fac Engn Khorramabad Iran

This paper presents a multi objective optimal location of AVRs in distribution systems at the presence of distributed generators based on modified teaching-learning-based optimization (MTLBO) algorithm. In the proposed MTLBO algorithm, teacher and learner phases are modified. The proposed objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm utilizes several teachers and considers the teachers as an external repository to save found Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as GA, PSO and TLBO, is extraordinary.

关键词： modified teaching-learning-based optimization Multi objective Automatic voltage regulator Pareto method

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：