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Multi-level production planning in a petrochemical industry using elitist teaching-learning-based-optimization

EXPERT SYSTEMS WITH APPLICATIONS 2015年第1期42卷 628-641页

作者： Kadambur, Rajasekhar Kotecha, Prakash Indian Inst Technol Dept Chem Engn Gauhati 781039 Assam India

The complex nature of the petrochemical industries necessitates an efficient decision on a large number of factors so as to optimally operate a plant. Production planning is an integral part of the petrochemical industry and requires the optimal selection of processes, production levels and products to maximize its profit. Previously an MILP formulation has been proposed for guiding the petrochemical industry development in Saudi Arabia (Alfares & Al-Amer, 2002). In this article, we state the limitations of this formulation and propose an alternate elitist TLBO algorithm based strategy to overcome them. The benefits of this strategy include the determination of better production plans that lead to higher profits and have been demonstrated on the eight case studies in the literature. The proposed strategy is generic and can be applied to determine production plans of multiple levels in various industries. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Combinatorial optimization Production planning teaching learning based optimization

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Power Quality Improvement with Shunt Active Filter under Various Mains Voltage Using teaching learning based optimization

Power Quality Improvement with Shunt Active Filter under Var...

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International Conference on Recent Advances in Engineering & Computational Sciences

作者： Soumya Mishra Pravat Kumar Ray Department of Electrical Enggineering National Institute of Technology

ISBN: (纸本)9781467382540

In this paper, instantaneous reactive power theory based a modified control algorithm is implemented for 3-phase 3-wire shunt active power filter to suppress harmonics and reactive power under non ideal source voltage. The reference current signals are generated by sensing the source voltage, load current and dc bus voltage. With these signals the gate driving pulses are generated via hysteresis band current controller. To check robustness of the control algorithm four different cases such as unbalanced, balanced distorted, unbalanced distorted main voltage and faults on load are considered. For more improvement in the performance of active filter, the Active filter is optimized with teaching-learning-based optimization. The simulation work is carried out in MATLAB/SIMULINK Power system block set. The performance of the implemented algorithm with teaching-learning-based optimization is found quite effective and satisfactory under these conditions.

关键词： Shunt active power filter teaching learning based optimization Total harmonic distortion Instantaneous power theory

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Multiuser Detection over Generalized-K Fading Channels using Two-Stage Initialized teaching learning based optimization

Multiuser Detection over Generalized-K Fading Channels using...

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International Conference on Advances in Computing, Communications and Informatics

作者： Lakshmi Manasa Gondela Vinay Kumar Pamula Anil Kumar Tipparti Department of Electronics and Communication Engineering University College of Engineering Kakinada JNTUK Kakinada India 533003 Department of Electronics and Communication Engineering MVSR Engineering College Hyderabad India 501510

ISBN: (纸本)9781509020300

A multiuser detection (MUD) technique for direct sequence-code division multiple access (DS-CDMA) systems over generalized-K (GK) fading channels using teaching learning based optimization algorithm (TLBO) with two-stage initialization (TSI) is proposed. In DS-CDMA systems, MUD techniques are applied to combat multiple access interference (MAI) and ambient noise. Empirical results proved that the ambient noise is non-Gaussian and impulse in nature which degrades the performance of the system substantially. The DS-CDMA signals are transmitted over channels that introduce impulsive noise, shadowing and fading. In this paper, we develop least-squares (LS), Huber and Hampel M-estimation based MUD technique for joint detection of DS-CDMA signals in the presence of MAI, impulsive noise, modeled by Laplace distribution, and channel fading, modeled by GK distribution. The TLBO with TSI (TLBO-TSI) algorithm is used to minimize a penalty function that is a less rapidly increasing function of residuals. Average bit error rate (BER) is computed to assess the performance of the TLBO-TSI based detector. Obtained results demonstrate that the proposed robust technique offer significant performance gains with increase in signal-to-noise ratio (SNR) and diversity order in the presence of heavy-tailed impulsive noise.

关键词： Bit error rate Direct sequence-code division multiple access Generalized-K distribution Multi user detection teaching learning based optimization

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Optimal Combined Overcurrent and Distance Relays Coordination using teaching learning based optimization

Optimal Combined Overcurrent and Distance Relays Coordinatio...

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IEEE India Council International Conference

作者： Saptarshi Roy P. Suresh Babu N. V. Phanendra Babu National Institute of Technology

ISBN: (纸本)9781538643198

Relay coordination is an important aspect to maintain proper power system operation and control. Relays should be organized in such a way that every relay should have a backup and Coordination time interval (CTI) between primary and back up and different zones of the relay should be maintained to achieve proper fault identification and fault clearance sequence. The relays should operate in minimum desirable time satisfying all the co-ordination constraints. So, relay coordination is nothing but highly constraint problem. Heuristic techniques are often used to get optimal solution of this kind of problem. In this paper this constraint problem is solved by teaching learning based optimization TLBO) on a WSCC-3-Machine-9bus system. Proper desirable time setting multiplier (TSM) with minimum operating time of relays are calculated. We also incorporated intelligent over current relay characteristics selection to get the desired results in this work. The results seem to be satisfactory as the results obtained from TLBO are comparatively better than so called conventional methods like Genetic Algorithm(GA) and Particle Swarm optimization (PSO).

关键词： Coordination of relay Coordination time interval teaching learning based optimization Plug setting Time setting multiplier Over current relay characteristics

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teaching-learning based optimization with global crossover for global optimization problems

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APPLIED MATHEMATICS AND COMPUTATION 2015年 265卷 533-556页

作者： Ouyang, Hai-bin Gao, Li-qun Kong, Xiang-yong Zou, De-xuan Li, Steven Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne Vic 3000 Australia

teaching learning based optimization (TLBO) is a newly developed population based meta heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： teaching learning based optimization Global optimization Crossover

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

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INFORMATION SCIENCES 2016年 357卷 182-200页

作者： Patel, Vivek K. Savsani, Vimal J. Gujarat Technol Univ Ahmadabad Gujarat India Pandit Deendayal Petr Univ Gandhinagar 382007 Gujarat India

This paper presents an efficient multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The proposed algorithm uses a grid-based approach in order to keep diversity in the external archive. Pareto dominance is incorporated into the MO-ITLBO algorithm in order to allow this heuristic to handle problems with several objective functions. The qualities of the solution are computed based on the Pareto dominance notion. The performance of the MO-ITLBO algorithm is assessed by applying it on a set of standard test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The results obtained using the proposed algorithm is compared with the other state-of-the-art algorithms available in the literature. Moreover, the performance of the MO-ITLBO algorithm is also compared with the multi-objective version of the basic teaching-learning based optimization algorithm (MO-TLBO). The statistical analysis of the experimental work is also carried out by conducting Friedman's rank test and Holm post hoc procedure. The results show that the proposed approach is competitive and effective compared to other algorithms contemplated in this work and it can also find the result with greater precision. (C) 2014 Elsevier Inc. All rights reserved.

关键词： teaching learning based optimization Improved teaching learning based optimization Multi-objective optimization Inverted generational distance

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An Improved teaching-learning-based optimization Algorithm Applied to the Design of Linear Phase Digital FIR Filter 2

An Improved Teaching-Learning-Based Optimization Algorithm A...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sharma, Shekhar Katiyal, Sumant Arya, L. D. SGSITS Dept Elect & Telecom Engn Indore Madhya Pradesh India Devi Ahilya Vishvavidyalaya Sch Elect Indore Madhya Pradesh India SGSITS Dept Elect Engn Indore Madhya Pradesh India

ISBN: (纸本)9789380544168

optimization algorithms based on evolutionary techniques have been applied for the optimal design of the linear phase digital FIR filter for better control of the parameters of it. The coefficients of the actual filter are optimized by these algorithms to approximate the frequency response of the desired filter. In this paper, an improved teaching-learning-based optimization (ITLBO) algorithm is applied for the design of linear phase digital FIR filter and its results are compared with those obtained by using teaching-learning-based optimization (TLBO) algorithm. The simulation results and comparison show that fast convergence is obtained in the both the cases of TLBO and ITLBO algorithms;however, the approximation error is smaller in the case of ITLBO algorithm. When frequency responses obtained using these algorithms are compared with those obtained using least-squares (LS) method of FIR filter design, better performance is shown by ITLBO algorithm.

关键词： Differential Evolution FIR filters Frequency response Least-squares method teaching learning based optimization

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Comparative Performance Evaluation of teaching learning based optimization against Genetic Algorithm on Benchmark Functions

Comparative Performance Evaluation of Teaching Learning Base...

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IEEE Power, Communication and Information Technology Conference

作者： M. Ebraheem T. R. Jyothsna Dept. of Electrical and Electronics Engg. GITAM Institute of Technology GITAM University Dept. of Electrical Engineering Andhra University college of Engineering AU

ISBN: (纸本)9781479974566

In this paper effectiveness of newly introduced 'teaching learning based optimization' (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.

关键词： teaching learning based optimization Genetic Algorithm Benchmark functions

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optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm

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ENERGY 2023年第1期285卷

作者： Toopshekan, Ashkan Abedian, Ali Azizi, Arian Ahmadi, Esmaeil Rad, Mohammad Amin Vaziri Univ Tehran Fac New Sci & Technol Dept Renewable Energies Tehran Iran Shahid Beheshti Univ Dept Elect Engn Tehran Iran Univ Texas Dallas Dept Elect & Comp Engn Richardson TX USA Kyoto Univ Grad Sch Energy Sci Kyoto Japan

Using optimization algorithms and developing dispatch strategies are essential in sizing renewable energy systems to ensure optimal performance, cost-effectiveness, and sustainability. This study employs the teaching learning-based optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The optimization results are validated using the Genetic Algorithm (GA) and Particle Swarm optimization (PSO). Furthermore, a novel dispatch strategy is developed to make an informed decision when using different energy sources. The strategy considers a 24-h foresight of upcoming electrical demand, solar irradiation, temperature, and wind speed. The developed dispatch strategy has led to a reduction in cost and excess electricity compared to the pre-prepared strategies. The energy sources employed include Photovoltaic panels (PV), Wind Turbines (WT), Diesel Generators (DG) with heat recovery capability, battery banks, and boilers to supply electrical and thermal demand. A Levelized cost of energy (LCOE) of 0.142 $/kWh is obtained for the PV/WT/DG/Battery/Boiler system. Although the three algorithms find almost similar optimal solutions, TLBO exhibits better convergence speed than PSO and GA. A comparison with HOMER software control strategies shows the developed dispatch strategy is 3.4% and 15.5% more efficient than Cycle Charging and Load Following strategies, respectively. Lastly, a comprehensive economic sensitivity analysis is performed to investigate the effect of inflation and discount rates on the size of components and final objective functions.

关键词： Renewable energy Forecasting dispatch teaching learning based optimization Combined heat and power

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Socio-Inspired Multi-Cohort Intelligence and teaching-learning-based optimization for Hydraulic Fracturing Parameters Design in Tight Formations

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第7期144卷 073201-073201页

作者： Muther, Temoor Syed, Fahad Iqbal Dahaghi, Amirmasoud Kalantari Negahban, Shahin Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA

Hydraulic fracturing is one of the revolutionary technologies widely applied to develop tight hydrocarbon reservoirs. Moreover, hydraulic fracture design optimization is an essential step to optimize production from tight reservoirs. This study presents the implementation of three new socio-inspired algorithms on hydraulic fracturing optimization. The work integrates reservoir simulation, artificial neural networks, and preceding optimization algorithms to attain the optimized fractures. For this study, a tight gas production dataset is initially generated numerically for a defined set of the fracture half-length, fracture height, fracture width, fracture conductivity, and the number of fractures' values. Secondly, the generated dataset is trained through a neural network to predict the effects of preceding parameters on gas production. Lastly, three new socio-inspired algorithms including cohort intelligence (CI), multi-cohort intelligence (multi-CI), and teaching learning-based optimization (TLBO) are applied to the regressor output to obtain optimized gas production performance with the combination of optimum fracture design parameters. The results are then compared with the traditionally used optimizers including particle swarm optimization (PSO) and genetic algorithm (GA). The results demonstrated that the multi-CI and TLBO converge at the global best position more often with a success rate of at least 95% as compared to CI, PSO, and GA. Moreover, the CI, PSO, and GA are found to stuck many times at the local maximum. This concludes that the multi-CI and TLBO are good alternatives to PSO and GA considering their high performance in determining the optimum fracture design parameters in comparison.

关键词： fracture half-length fracture conductivity hydraulic fracture optimization particle swarm optimization genetic algorithm cohort intelligence teaching learning based optimization hydrates coal bed methane heavy oil oil sands tight gas oil gas reservoirs petroleum engineering petroleum wells-drilling production construction unconventional petroleum

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