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teaching-learning-based optimisation algorithm and its application in capturing critical slip surface in slope stability analysis

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SOFT COMPUTING 2020年第4期24卷 2969-2982页

作者： Mishra, Mayank Gunturi, Venkata Ramana Maity, Damodar Indian Inst Technol Dept Civil Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Civil Engn Delhi India

The identification of ideal values for algorithm-specific parameters required for the functioning of metaheuristic approaches at their optimum performance is a difficult task. This paper presents the application of a recently proposed teaching-learning-based optimisation (TLBO) algorithm to determine the lowest factor of safety (FS) along a critical slip surface for soil slope. TLBO is a nature-inspired search algorithm based on the teaching-learning phenomenon of a classroom. Four benchmark slopes are reanalysed to test the performance of the TLBO approach. The results indicate that the present technique can detect the critical failure surface and can be easily implemented by practitioners without fine-tuning the parameters that affect the convergence of results. Statistical analyses indicate a drastic decrease in uncertainty and the number of function evaluations in the estimation of the FS over previous approaches.

关键词： teaching-learning-based optimisation Landslide Slope stability analysis Critical failure surface Swarm intelligence

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Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation

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IET NANOBIOTECHNOLOGY 2014年第4期8卷 238-246页

作者： Cheng, Yu-Huei Toko Univ Dept Digital Content Design & Management Chiayi Taiwan

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, teaching-learning-based optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150-300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method.

关键词： biochemistry biology computing learning (artificial intelligence) molecular biophysics nanobiotechnology optimisation computational intelligence-based method polymerase chain reaction primer selection teaching-learning-based optimisation melting temperature nucleotide sequences Homo species genetic algorithm memetic algorithm Primer3 method automatic high-throughput analysis

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A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 37卷 279-292页

作者： Li, Jun-qing Pan, Quan-ke Mao, Kun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

In this study, we proposed a discrete teaching-learning-based optimisation (DTLBO) for solving the flowshop rescheduling problem. Five types of disruption events, namely machine breakdown, new job arrival, cancellation of jobs, job processing variation and job release variation, are considered simultaneously. The proposed algorithm aims to minimise two objectives, i.e., the maximal completion time and the instability performance. Four discretisation operators are developed for the teaching phase and learning phase to enable the TLBO algorithm to solve rescheduling problems. In addition, a modified iterated greedy (IG)-based local search is embedded to enhance the searching ability of the proposed algorithm. Furthermore, four types of DTLBO algorithms are developed to make detailed comparisons with different parameters. Experimental comparisons on 90 realistic flowshop rescheduling instances with other efficient algorithms indicate that the proposed algorithm is competitive in terms of its searching quality, robustness, and efficiency. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Flowshop problem Multi-objective teaching-learning-based optimisation Rescheduling

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Performance improvement of teaching-learning-based optimisation for robust machine layout design

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 98卷 129-152页

作者： Vitayasak, Srisatja Pongcharoen, Pupong Naresuan Univ Fac Engn CORIA Dept Ind Engn Phitsanulok 65000 Thailand

teaching-learning-based optimisation (TLBO) is one of the more recently developed metaheuristics and has been successfully applied to solve various optimisation problems. However, TLBO has not been academically reported for solving the robust machine layout design (MLD) problems with dynamic demand. Considering internal logistics activities, shortening material flow distance within a manufacturing area can lead to efficient productivity and a decrease in related costs. The robust machine layout is concerned with determining the efficient arrangement of machines/facilities located on a manufacturing shop floor under future demand fluctuation. A robust designed layout is essential for a company to maintain a high productivity rate through multiple time-periods of demand uncertainty with minimum effects related to the re-layout time and cost, manufacturing disruption, and the movement of monument machines. The objectives of this paper were to: i) describe the development of a computer aided layout designing tool for minimising the total material flow distance under dynamic demand scenario, ii) investigate the appropriate setting of TLBO parameters, and iii) propose four TLBO modifications for improving its performance. The modified TLBOs were inspired by multiple teachers with two types of classes and two approaches to teacher selection. The numerical experiments were designed and conducted using eleven MLD bench marking datasets. Statistical analyses on the experimental results showed a superior performance for the proposed modifications. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Computational intelligence teaching-learning-based optimisation Facility layout Robust design Dynamic demand

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Cooperated teaching-learning-based optimisation for distributed two-stage assembly flow shop scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第23期59卷 7232-7245页

作者： Lei, Deming Su, Bin Li, Ming Wuhan Univ Technol Sch Automat Wuhan Peoples R China

Two-stage assembly flow shop scheduling problem with DPm -> 1 layout has been extensively considered in single factory;however, distributed two-stage assembly flow shop scheduling problem (DTAFSP) with DPm -> 1 layout in each factory is not studied fully;moreover, teaching-learning-based optimisation is seldom used to solve DTAFSP. In this paper, a cooperated teaching-learning-based optimisation (CTLBO) is proposed to minimise makespan. Multiple classes are constructed. The whole search procedure consists of two stages and each stage possesses two teacher's phases and a learner phase. Class cooperation between the best class and the worst one is implemented by exchanging search times and search ability at the second stage and seldom adopted in the existing works. Extensive experiments are conducted and CTLBO is compared with the existing methods to test its performances. Computational results demonstrate that CTLBO has very competitive performances on solving the considered DTAFSP.

关键词： Scheduling flow shop teaching-learning-based optimisation assembly distributed scheduling

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Optimal setting of TCSCs in power systems using teaching-learning-based optimisation algorithm

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NEURAL COMPUTING & APPLICATIONS 2015年第5期26卷 1249-1256页

作者： Jordehi, A. Rezaee Univ Putra Malaysia Dept Elect Engn Serdang 43400 Selangor Malaysia

teaching-learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas. In this study, TLBO is assisted to find optimal setting of thyristor-controlled series compensators in electric power systems. The experiments have been done for both N-1 and N-2 line outage contingencies. The results show that TLBO performs well in solving this problem.

关键词： teaching-learning-based optimisation FACTS allocation problem Contingency

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A modified teaching-learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第12期54卷 3622-3639页

作者： Shen, Jing-nan Wang, Ling Zheng, Huan-yu Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a modified teaching-learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.

关键词： re-entrant hybrid flowshop scheduling bi-objective teaching-learning-based optimisation decoding method

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An effective teaching-learning-based optimisation algorithm for RCPSP with ordinal interval numbers

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第6期53卷 1777-1790页

作者： Zheng, Huan-yu Wang, Ling Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

To solve the resource-constrained project-scheduling problem (RCPSP) with ordinal interval numbers, this paper presents an effective teaching-learning-based optimisation (TLBO) algorithm. Ordinal interval number is introduced as a novel tool for handling vague information to describe the RCPSP under uncertain environment. An ordinal interval-based parallel schedule generation scheme is used to generate feasible schedules. Two new phases including the self-study phase and the exam phase are incorporated into the TLBO to enhance the teaching-learning process. In the self-study phase, the population is updated by a mutation operator to prevent premature convergence and to enhance exploration search. In the exam phase, elite students are selected to enhance exploitation search. Moreover, a novel ordinal interval resource-based crossover operator (OIRBCO) is well designed for both the teacher phase and the student phase of the TLBO. Computational comparisons between the OIRBCO and the existing two-point crossover show that OIRBCO is more effective due to the utilisation of the resource information. In addition, statistical comparisons with particle swarm optimisation and simulated annealing show that the proposed TLBO is more effective in solving the RCPSP with ordinal interval numbers.

关键词： teaching-learning-based optimisation ordinal interval resource-based crossover self-study phase ordinal interval number exam phase Resource-constrained project scheduling

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Total harmonic distortion minimisation in multilevel inverters using the teaching-learning-based optimisation algorithm

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INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2018年第3期39卷 264-269页

作者： Olamaei, Javad Karimi, Masoumeh Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran Azarbaijan Shahid Madani Univ Elect Engn Dept Distribut Grid Res Lab Tabriz Iran

In this article, the minimisation of total harmonic distortion (THD) of the output voltage of a multilevel inverter is studied. The harmonic component of output voltage in the inverter is reduced in THD minimisation. The output voltage THD in multilevel inverters is minimised by proper selection of switching angles. The teaching-learning-based optimisation algorithm is used to find the optimum switching angles in order to generate the desired voltage value in the possible minimum THD. Experimental and simulation results depict advantages of this approach compared to published papers which discus this concept using genetic algorithm. The experiments conducted on a seven-level inverter confirm the feasibility of this approach.

关键词： teaching-learning-based optimisation multilevel inverter minimisation of total harmonic distortion phase voltage

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Cooperative Designing of Machine Layout Using teaching learning based optimisation and Its Modifications 1

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17th International Conference on Cooperative Design, Visualization, and Engineering (CDVE)

作者： Vitayasak, Srisatja Pongcharoen, Pupong Naresuan Univ Fac Engn Ctr Operat Res & Ind Applicat Dept Ind Engn Phitsanulok 65000 Thailand

ISBN: (数字)9783030608163

ISBN: (纸本)9783030608156;9783030608163

A variation of customer demand over time periods has resulted in production layout's efficiency especially in term of material handling cost. Machine re-location approach can help to maintain the flow distances but the costs related to the machine movement may be imposed. Cooperative redesigning of machine layouts between time periods was proposed to minimise both material handling and relocation costs. In this work, teaching-learning-based optimisation (TLBO) and its modifications were applied to solve nonidentical machine layout redesign (MLRD) problem in multi-period multi-row configuration with demand uncertainty scenario. The computational experiments were carried out using eleven benchmarking datasets. The performance of the proposed methods was compared with the conventional Genetic Algorithm, Backtracking Search Algorithm. The effect of relocation cost on the layout design approach was also investigated.

关键词： Layout redesign Cooperative redesign teaching-learning-based optimisation Demand uncertainty

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