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A note on multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)

INFORMATION SCIENCES 2016年第0期373卷 337-350页

作者： Chinta, Sivadurgaprasad Kommadath, Remya Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India

Recently Multi-Objective Improved teaching-learning-based-optimization algorithm (MO-ITLBO) has been proposed to solve complex multi-objective optimization problems and has been shown to be competitive against various other state-of-the-art algorithms. The algorithm was demonstrated on the constrained and unconstrained optimization problems of CEC 2009 and was reported to have shown impressive results. However, some critical steps in the algorithm have not been adequately described, and these have become major impediments even for the implementation of MO-ITLBO. In this note, we have explained all such issues which need to be convincingly addressed so that independent researchers could evaluate and use MO-ITLBO for various other applications. Also, two variants of MO-ITLBO have been suggested whose results enforce that the issues reported in this article are critical to harness the reported benefits of the MO-ITLBO. (C) 2016 Elsevier Inc. All rights reserved.

关键词： teaching-learning based optimization Improved teachinglearning based optimization Multi-objective optimization Pareto dominance Epsilon dominance

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Two-sided assembly line balancing using teaching-learning based optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 291-299页

作者： Tuncel, Gonca Aydin, Dilek Dokuz Eylul Univ Dept Ind Engn TR-35160 Izmir Turkey

Assembly line balancing plays a crucial role in modern manufacturing companies in terms of the growth in productivity and reduction in costs. The problem of assigning tasks to consecutive stations in such a way that one or more objectives are optimized subject to the required tasks, processing times and some specific constraints is called the assembly line balancing problem (ALBP). Depending on production tactics and distinguishing working conditions in practice, assembly line systems show a large diversity. Although, a growing number of researchers addressed ALBP over the past fifty years, real-world assembly systems which require practical extensions to be considered simultaneously have not been adequately handled. This study deals with an industrial assembly system belonging to the class of two-sided line with additional assignment restrictions which are often encountered in practice. teaching-learning based optimization (TLBO), which is a recently developed nature-inspired search method, is employed to solve the line balancing problem. Computational results are compared with the current situation in terms of the line efficiency, and the solution structure with workload assigned to the stations is presented. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Two-sided assembly lines Line balancing teaching-learning based optimization Application Manufacturing

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Minimization of total completion time on a batch processing machine with arbitrary release dates: an effectual teaching-learning based optimization approach

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2019年第5期13卷 557-566页

作者： Beldar, Pedram Framinan, Jose M. Ardakani, Allahyar Islamic Azad Univ Dept Ind & Mech Engn Qazvin Branch Qazvin Iran Univ Seville Ind Management Sch Engn Camino Descubrimientos Seville 41092 Spain Univ Tasmania Natl Ctr Ports & Shipping Australian Maritime Coll Hobart Tas Australia

In this research study, a single machine batch-processing problem with release dates to minimize the total completion times of jobs is considered. The machine is able to process at most a certain number of jobs at the same time and the total size of the jobs allocated to a batch cannot exceed the machine capacity. Since the research problem has been shown to be NP-hard, an effective teaching-learning based optimization (TLBO) is proposed. A constructive heuristic approach is developed to generate initial feasible solutions for the TLBO. In order to enhance the efficiency of the proposed TLBO, a Tabu Search (TS) with three different neighborhood generation mechanisms is incorporated into the teaching phase and learner phase separately. To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature. The computational results show that the proposed TLBO with the incorporation of TS in its learning phase is able to come up with very good quality solutions.

关键词： Total completion times minimization Batch processing Single machine scheduling teaching-learning based optimization Heuristic approach

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An Improved teaching-learning based optimization for optimization of Flatness of a Strip During a Coiling Process 10th

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10th Multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Bureerat, Sujin Pholdee, Nantiwat Park, Won-Woong Kim, Dong-Kyu Khon Kaen Univ Fac Engn Sustainable & Infrastruct Res & Dev Ctr Dept Mech Engn 123 Moo 16 Mittraphap Rd Khon Kaen Thailand Korea Adv Inst Sci & Technol Natl Res Lab Comp Aided Mat Proc Dept Mech Engn Daejeon South Korea Korea Atom Energy Res Inst Div Neutron Sci Daejeon South Korea

ISBN: (纸本)9783319493978;9783319493961

Performance enhancement of a teaching-learning basedz optimizer (TLBO) for strip flatness optimization during a coiling process is proposed. The method is termed improved teaching-learning based optimization (ITLBO). The new algorithm is achieved by modifying the teaching phase of the original TLBO. The design problem is set to find spool geometry and coiling tension in order to minimize flatness defects during the coiling process. Having implemented the new optimizer with flatness optimization for strip coiling, the results reveal that the proposed method gives a better optimum solution compared to the present state-of-the-art methods.

关键词： Evolutionary algorithm Flatness defect optimization Strip coiling teaching-learning based optimization

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Group Leader Dominated teaching-learning based optimization 14

Group Leader Dominated Teaching-Learning based Optimization

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14th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Chen, Chang-Huang Tungnan Univ Dept Elect Engn New Taipei City 222 Taiwan

ISBN: (纸本)9781479924189

teaching-learning based optimization (TLBO), inspired from the teaching-learning process in a classroom, is a newly developed population based algorithm. Except population size and maximum number of iteration, it does not require any specific parameters. TLBO consists of two modes of searching phase, teacher and learner phase. In this paper, every learner is assigned to at least one groups and, instead of a learner studied by interacting directly with other learners, group leader is responsible for raising up the member's knowledge, i.e., to explore for optimal solution. The idea is analog to group discussion in which group leader always dominate group discussion direction and performance. For simplicity, the proposed algorithm will be denoted as LTLBO. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with original TLBO and particle swarm optimization (PSO).

关键词： Neighborhood structure Particle swarm optimization Population-based optimization teaching-learning based optimization

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Optimal incorporation of solar and wind DGs along with seasonal loads using teaching-learning based optimization 5

Optimal incorporation of solar and wind DGs along with seaso...

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IEEE 5th Conference on Knowledge based Engineering and Innovation (KBEI)

作者： Kani, Farzad Fallah Arbani, Mohammad Dameshghi Esmaeli, Abdolreza Univ Maziar Dept Elect Engn Noor Iran Azad Univ Dept Elect Engn Tafresh Iran Nucl Sci & Technol Res Inst Plasma & Nucl Fus Res Sch Tehran Iran

ISBN: (纸本)9781728108728

Renewable energy attracts more attention every day, because they are non-polluting and lasting. Recently, a significant number of renewable energy generating units (DGs) that are based on intermittent generation patterns are being added to the distribution network, in addition to the growing need for load, environmental threats are As well as reach the minimum. An appropriate combination of distributed disposable generation in distribution networks is important to guarantee the operational benefits of network quality. In this paper, the optimal location of solar sources and wind sources in a distribution network, taking into account seasonal loads and atmospheric changes has been made. Modeling has been made due to uncertainty in solar beam and wind speed. For better stability and improvement of power quality, we consider constraints on the voltage and current flow of transmission lines and the problem of optimizing the location of resources with these constraints has become a multi-objective optimization problem. To solve the problem, the optimization method teaching-learning based optimization (TLBO) has been used. The proposed method has been tested and tested on a standard 30-bus network. According to simulation results, the proposed method has had satisfactory and promising results.

关键词： distributed generation solar-wind hybrid system teaching-learning based optimization

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Hybridizing teaching-learning based optimization with GA and PSO: Case Study of Supply Chain Network Model

Hybridizing Teaching-Learning Based Optimization with GA and...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Gen, Mitsuo Anudari, Chuluunsukh Yun, YoungSu Fuzzy Log Syst Inst Tokyo Japan Tokyo Univ Sci Tokyo Japan Chosun Univ Int Cooperat Team Gwangju South Korea Chosun Univ Sch Business Adm Gwangju South Korea

ISBN: (纸本)9781665458412

Design and optimization of logistics and supply chain management (SCM) network is a very important issue, which plans, implements and controls the efficient, effective forward and reverses flows and storage of goods, services and related information between the point of origin and the point of consumption to meet customers' requirements. The structure of each stage of the supply chain is completed by a set of arcs connected to the nodes and representing the flow of goods: each arc has a weight proportional to the cost incurred in between the two nodes it connects together. In this paper, we first formulate the nonlinear 0-1 integer programming model of the SC network consisting of suppliers, manufacturers, DCs, and customers. Secondary we propose a hybridized teaching-learning based optimization (TLBO) with genetic algorithm (GA) and particle swarm optimization (PSO) combined Cauchy distribution to solve the SC network models. Lastly, we demonstrate the proposed GA+PSO+TLBO method outperformed GA, TLBO, GA+TLBO under the same computational environment for two numerical experiments of the several scaled SC network models.

关键词： Supply chain network hybrid metaheuristic approach genetic algorithm particle swarm optimization teaching-learning based optimization

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Optimal Operational Scheduling of Renewable Energy Sources Using teaching-learning based optimization Algorithm by Virtual Power Plant

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2017年第6期139卷 062003-062003页

作者： Kasaei, Mohammad Javad Gandomkar, Majid Nikoukar, Javad Islamic Azad Univ Dept Elect Engn Coll Elect Engn & Comp Saveh Branch Saveh Iran

In recent years, a large number of renewable energy sources (RESs) have been added into modern distribution systems because of their clean and renewable property. Nevertheless, the high penetration of RESs and intermittent nature of some resources such as wind power and photovoltaic (PV) cause the variable generation and uncertainty of power system. In this condition, one idea to solve problems due to the variable output of these resources is to aggregate them together. A collection of distributed generations (DGs) such as wind turbine (WT), PV panel, fuel cell (FC), and any other sources of power, energy storage systems, and controllable loads that are aggregated together and are managed by an energy management system (EMS) are called a virtual power plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost for a 24-h period. To solve the problem, a metaheuristic optimization algorithm, teaching-learning based optimization (TLBO), is proposed to determine optimal management of RESs, storage battery, and load control in a real case study.

关键词： virtual power plant renewable energy sources optimal management of energy operating cost teaching-learning based optimization

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Multi-criteria evolutionary optimization of a traffic light using genetics algorithm and teaching-learning based optimization

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EXPERT SYSTEMS 2024年第2期41卷 e13487-e13487页

作者： Yektamoghadam, Hossein Nikoofard, Amirhossein Behzadi, Masoumeh Khosravy, Mahdi Dey, Nilanjan Witkowski, Olaf Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran KN Toosi Univ Technol Dept Elect Engn Tehran Iran Cross Compass Ltd Cross Labs Tokyo Japan Techno Int New Town Dept Comp Sci & Engn Kolkata India

Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi-criteria decision-making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub-optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and teaching-learning-based optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real-world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.

关键词： evolutionary algorithm evolutionary optimization genetic algorithm teaching-learning based optimization traffic light control

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An Improved teaching-learning based optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

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IEEE ACCESS 2019年 7卷 136525-136534页

作者： Feng, Hailong Li, Qiuhong Nanjing Univ Aeronaut & Astronaut Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China

The modeling of the engine starting process is vital to ensure the successful start of the engine. However, the engine starting process is very complicated and challenging to model. To optimize the start model performance, an improved teaching-learning based optimization (ITLBO) algorithm is proposed. In ITLBO, a collective lesson preparation phase is increased to enhance the teaching ability of the teacher. The random learning phase is replaced by S-shape group learning, and students learn from the top students of their groups. Also the deterministic sampling selection phase is introduced to ITLBO, and the students with higher evaluation have more possibility to advance in class. The improved algorithm is tested on 18 benchmark functions. The results indicate that the proposed ITLBO algorithm performs much better in terms of convergence speed and accuracy than standard TLBO. When applied to the model adaptation of the turbofan engine starting process, ITLBO is used to optimize the speed line of the rotation components gradually from the lower speed line to the idle speed line. The weighted sum of relative errors between the model outputs and the start test data is taken as the fitness function. After adaptation, the maximum relative errors of model outputs to start test data are significantly decreased, which shows the effectiveness of the ITLBO in model adaption.

关键词： Education Adaptation models optimization Engines Heuristic algorithms Convergence Sociology Model adaptation teaching-learning based optimization collective lesson preparation turbofan engine

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