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23rd IEEE International Conference on Digital Signal Processing (DSP)

作者： Kwan, Hon Keung Zhang, Miao Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781538668122;9781538668115

In this paper, minimax design of circularly symmetric 2-dimensional finite impulse response digital filters using an elitist version of the teaching-learning-based optimization is presented. A discrete frequency grid based on octagonal symmetry design is used for optimization. Circularly symmetric 2 dimensional FIR lowpass digital filter of different filter sizes are designed and the results are given along with those obtained by a recent design method.

关键词： Circularly symmetric 2-D FIR filters 2-D FIR lowpass filters minimax design teaching-learning-based optimization

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An Improved Most Valuable Player Algorithm with Twice Training Mechanism 14th

An Improved Most Valuable Player Algorithm with Twice Traini...

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14th International Conference on Intelligent Computing (ICIC)

作者： Liu, Xin Luo, Qifang Wang, Dengyun Abdel-Baset, Mohamed Jiang, Shengqi Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China Zagazig Univ Fac Comp & Informat El Zera Sq Zagazig 44519 Sharqiyah Egypt

ISBN: (纸本)9783319959290;9783319959306

The most valuable player algorithm is inspired from these players who want to win the Most Valuable Player (MVP) trophy, it have higher overall success percentage. teaching-learning-based optimization (TLBO) simulates the process of teaching and learning. TLBO has fewer parameters that must be determined during the renewal process. This paper proposes twice training mechanism to enhance the search ability of the most valuable player algorithm (MVPA) through hybrid TLBO algorithm, and named it teaching the most valuable player algorithm (TMVPA). In TMVPA, designs two behaviors of training and abstract two training modes: pre-competition training and post-competition training. Before individual competition, join the pre-competition training to coordinated exploitation ability and the exploration ability of the original algorithm and join the post-competition training to prevent from falling into the local optimal field after the corporate competition. We test three benchmark functions and an engineering design problem. Results show that TMVPA has effectively raised algorithm accuracy.

关键词： Most valuable player algorithm Two training modes Benchmark functions teaching-learning-based optimization Engineering design problems

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Ordinal optimization based metaheuristic algorithm for optimal inventory policy of assemble-to-order systems

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APPLIED MATHEMATICAL MODELLING 2017年 42卷 43-57页

作者： Horng, Shih-Cheng Lin, Shieh-Shing Chaoyang Univ Technol Dept Comp Sci & Informat Engn 168 Jifong E Rd Taichung 41349 Taiwan

Assemble-to-order (ATO) systems refer to a manufacturing process in which a customer must first place an order before the ordered item is manufactured. An ATO system that operates under a continuous-review base-stock policy can be formulated as a stochastic simulation optimization problem (SSOP) with a huge search space, which is known as NP hard. This work develops an ordinal optimization (OO) based metaheuristic algorithm, abbreviated to OOMH, to determine a near-optimal design (target inventory level) in ATO systems. The proposed approach covers three main modules, which are meta-modeling, exploration, and exploitation. In the meta-modeling module, the extreme learning machine (ELM) is used as a meta-model to estimate the approximate objective value of a design. In the exploration module, the elite teaching-learning-based optimization (TLBO) approach is utilized to select N candidate designs from the entire search space, where the fitness of a design is evaluated using the ELM. In the exploitation module, the sequential ranking and -selection (R&S) scheme is used to optimally allocate the computing resource and budget for effective selecting the critical designs from the N candidate designs. Finally, the proposed algorithm is applied to two general ATO systems. The large ATO system comprises 12 items on eight products and the moderately sized ATO system is composed of eight items on five products. Test results that are obtained using the OOMH approach are compared with those obtained using three heuristic methods and a discrete optimization via -simulation (DOvS) algorithm. Analytical results reveal that the proposed method yields solutions of much higher quality with a much higher computational efficiency than the three heuristic methods and the DOvS algorithm. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Assemble-to-order system Continuous-review base-stock policy Ordinal optimization Extreme learning machine teaching-learning-based optimization Ranking and selection

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Multi-objective optimization of machining parameters in multi-pass turning operations for low-carbon manufacturing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第13期231卷 2372-2383页

作者： Lin, Wenwen Yu, Daoyuan Zhang, Chaoyong Zhang, Sanqiang Tian, Yuhui Liu, Shengqiang Luo, Min Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Hubei Univ Technol Hubei Agr Machinery Engn Res & Design Inst Wuhan Hubei Peoples R China Shandong Hoteam Software Co Ltd Jinan Shandong Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan Shandong Peoples R China Hubei Univ Automot Technol Coll Elect & Informat Engn Shiyan Peoples R China

Most recent studies on machining parameter optimization in machining operations focused on reducing machining cost and energy consumption. However, environmental impacts caused by manufacturing activities were not involved in those studies, which can be quantified by equivalent carbon dioxide emissions. In this study, a direct method was proposed to quantify the carbon emissions generated during multi-pass turning operations. Moreover, machining parameter optimization models of multi-pass turning operations in dry and wet cut environments were established using an experimental design method. Three objectives were considered in both models: carbon emissions, operation time, and machining cost. Furthermore, a multi-objective teaching-learning-based optimization algorithm was used to deal with the models. The optimization results indicated that the use of cutting fluids could significantly reduce carbon emissions and machining cost and improve production efficiency in multi-pass turning operations.

关键词： Machining parameter optimization multi-pass turning operation energy consumption carbon emissions teaching-learning-based optimization

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Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization

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FUEL 2017年 207卷 547-560页

作者： Choubineh, Abouzar Ghorbani, Hamzeh Wood, David A. Moosavi, Seyedeh Robab Khalafi, Elias Sadatshojaei, Erfan Petr Univ Technol Petr Dept Ahvaz Iran Islamic Azad Univ Young Researchers Club Omidiyeh Branch Omidiyeh Iran DWA Energy Ltd Lincoln England Shiraz Univ Petr Dept Shiraz Iran

Published relationships typically consider liquid critical-flow rate through wellhead chokes of producing oil wells as functions of wellhead pressure, choke size and gas-liquid ratio. Such correlations can be improved by taking into account three additional input variables: gas specific gravity, oil specific gravity and temperature. Novel liquid critical-flow rate models, hybridizing an artificial neural network (ANN) with a teaching-learning-based optimization (TLBO) algorithms, involving 3 and 6 input variables, demonstrate improved accuracy compared to nonlinear regression models, traditional ANN models and published correlations. The improved accuracy of the developed models is assessed statistically using a data set of 113 wellhead flow tests from oil wells in South Iran (with a full data listing included). The ANN-TLBO (6 parameters) developed model is the most accurate, yielding the best liquid critical-flow rate predictions for that data set: coefficient of determination of 0.981;root mean square error of 714;average relative error of 2.09%;and, average absolute relative error of 6.5%. The 6-parameters models outperform the 3-parameters models without over complicating model functionality. This justifies the consideration of all six input variables to deliver improved predictions of wellhead choke liquid critical-flow rates. Calculation of relevancy factors for the 6-parameters ANN-TLBO model to the data set for all six input variables reveals choke size and gas-liquid ratio have maximum and minimum influence in determining the liquid critical-flow rate, respectively. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Liquid critical-flow rate Non-linear regression Artificial neural network teaching-learning-based optimization Empirical wellhead coke flow rate published correlations Relevancy factor

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Application of Multi-Objective teaching learning based optimization Algorithm to Optimal Power Flow Problem

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Procedia Technology 2012年 6卷 255-264页

作者： M.R. Nayak C.K. Nayak P.K. Rout Deptt. of Electrical Engg Institute of Technical Education & Research S‘O’A University Bhubaneswar 751030 Odisha India Deptt. of EEE Institute of Technical Education & Research S‘O’A University Bhubaneswar 751030 Odisha India

This paper presents a non-domination based sorting multiobjective teaching-learning-based optimization algorithm, for solving the optimal power flow (OPF) problem. The OPF problem is a nonlinear constrained multi-objective optimization problem where the fuel cost, Transmission losses and L-index are to be minimized. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The standard IEEE 30-bus test system is used and the results show the effectiveness of MOTLBO and confirm its potential to solve the multi-objective OPF problem. Simulation results clearly show that the proposed method is able to produce true and well distributed Pareto optimal solutions for multiobjective OPF problem and the comparison with the results reported in the literature demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective OPF problem.

关键词： Multi-objective Optimal Power Flow Pareto optimal front Non-dominated sorting teaching-learning-based optimization

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ETLBO based optimal targeting to the moon in the PCR3BP chaotic system

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CHAOS SOLITONS & FRACTALS 2017年 105卷 21-28页

作者： Wang, Yang Pan, Binfeng Zheng, Yue Lu, Xiang Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China

The chaotic transport in Earth-Moon three-body system has been demonstrated to be a novel approach to explore the Moon at a low energy cost. However, existing targeting methods require sufficient experience to construct Earth-Moon chaotic transfer orbits, which is not an easy task to the untrained eye. In this paper, the elitist teaching-learning-based optimization (ETLBO) based optimal targeting method is presented to provide a systematic approach to design the chaotic transfer orbits in the Earth-Moon planar circular restricted three-body problem, without any requirement of prior experience. Unlike the existing targeting methods, the chaotic transfer orbits design problem is treated as a class of multi-constraints fuel-optimal problem with multi-dimensional decision variables. A discrete chaotic dynamical model is formulated according to the Poincare map, and several consecutive control steps of small bounded thrusts are made to direct the chaotic series towards the desired invariant torus near the Moon. The suboptimal consecutive control thrusts are obtained by a state-of-art numerical optimization algorithm ETLBO, which does not require any algorithm-specific parameters with less computational effort. Numerical demonstrates are provided to illustrate the applications of the ETLBO based optimal targeting method, which reveal that several potential chaotic transfer orbits can be easily obtained by this method. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Chaos control teaching-learning-based optimization Earth-Moon transfer Optimal targeting

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An Adaptive Extreme learning Machine for Modeling NOx Emission of a 300 MW Circulating Fluidized Bed Boiler

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NEURAL PROCESSING LETTERS 2017年第2期46卷 643-662页

作者： Li, Xia Niu, Peifeng Li, Guoqiang Liu, Jianping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Hebei Normal Univ Sci & Technol Qinhuangdao 066004 Peoples R China

Extreme learning machine (ELM) provides high learning speed, but generalization performance needs to be further improved. Therefore, we propose an adaptive ELM with a relaxation factor lambda (A-ELM). In A-ELM, according to the nonlinear degree of actual data, the output layer obtains adaptively 1 - lambda rate information through the hidden layer and lambda rate information through the input layer. Since the relaxation factor. is bound up with the input weights and hidden biases of A-ELM, in order to obtain the optimal lambda, lambda, input weights and hidden biases are obtained together by teaching-learning-based optimization (A-ELM-TLBO). Then, 15 benchmark regression data sets verify the performance of A-ELM-TLBO. Finally, A-ELM-TLBO is applied to set up the mapping relation between NOx emission and operational conditions of a 300 MW circulating fluidized bed (CFB) boiler. Compared with six other models, experimental results show that A-ELM-TLBO has good approximation ability and generalization performance. So, A-ELM-TLBO provides a good basis for tuning CFB boiler operating parameters to reduce NOx emission.

关键词： Extreme learning machine Relaxation factor teaching-learning-based optimization NOx emission Circulating fluidized bed boiler

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Optimal Allocation of DGs and Reconfiguration of Radial Distribution Systems Using an Intelligent Search-based TLBO

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第5期45卷 476-490页

作者： Kanwar, Neeraj Gupta, Nikhil Niazi, Khaleequr R. Swarnkar, Anil Malaviya Natl Inst Technol Dept Elect Engn JLN Marg Jaipur 302017 Rajasthan India

This paper addresses an application of teaching-learning-based optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions.

关键词： distributed generations intelligent search network reconfiguration radial distribution systems search space reduction teaching-learning-based optimization

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Harmony search with teaching-learning algorithm for nonnegative linear least square

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2017年第8期20卷 1661-1677页

作者： Yong, Longquan Tuo, Shouheng Gao, Kai Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China

Nonnegative linear least square (NLLS) is an important optimization problem, which often appears in processing the physical data. In this paper, we use a recently proposed algorithm named harmony search with teaching-learning (HSTL) strategy which derived from teaching-learning-based optimization (TLBO) for solving NLLS problems. Numerical results are reported which demonstrate very good computational performance.

关键词： Nonnegative linear least square harmony search teaching-learning-based optimization

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