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A novel chaotic teaching learning based optimization algorithm and its application in optimization of extreme learning machine

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Journal of Physics: Conference Series 2021年第1期2003卷 012003-012003页

作者： Ma, Yunpeng Tang, Haoheng Wang, Heqi Wang, Zhenying Zhang, Xinxin Li, Lipeng School of Information Engineering Tianjin University of Commerce TianjinCO300134 China

Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement. © Published under licence by IOP Publishing Ltd.

关键词： Chaos Theory Extreme learning Machine Model optimization teaching learning based optimization algorithm

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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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Load frequency control of an autonomous hybrid power system by quasi-oppositional harmony search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 78卷 715-734页

作者： Shankar, G. Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

This paper deals with a novel quasi-oppositional harmony search algorithm (QOHSA) based design of load frequency controller for an autonomous hybrid power system model (HPSM) consisting of multiple power generating units and energy storage units. QOHSA is a novel improved version of music inspired harmony search algorithm for obtaining the best solution vectors and faster convergence rate. In this paper, the efficacy of the proposed QOHSA is adjudged for optimized load frequency control (LFC) of an autonomous HPSM. The studied HPSM consists of renewable/non-renewable energy based generating units such as wind turbine generator, solar photovoltaic, solar thermal power generator, diesel engine generator, fuel cell with aqua-electrolyzer while energy storage units consists of battery energy storage system, flywheel energy storage system and ultra-capacitor. Gains of the conventional controllers such as integral (I) controller, proportional-integral (PI) controller and proportional-integral-derivative (PID) controller (installed as frequency controller one at a time in the proposed HPSM) is optimized using QOHSA to mitigate any frequency deviation owing to sudden generation/load change. In order to corroborate the efficacy of QOHSA, performance of QOHSA to design optimal LFC is compared with that of other well-established technique such as teaching learning based optimization algorithm (TLBOA). The comparative performances of the HPSM under the action of QOHSA/TLBOA based optimized conventional controllers (I or PI or PID) are investigated and compared in the present work. It is found that the QOHSA tuned frequency controllers improves the overall dynamic response in terms of settling time, overshoot and undershoot in the profile of frequency deviation and power deviation of the studied HPSM. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Hybrid power system model Load frequency control Quasi-oppositional harmony search algorithm Renewable energy sources teaching learning based optimization algorithm

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A fast and robust procedure for optimal detail design of continuous RC beams

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COMPUTERS AND CONCRETE 2019年第4期24卷 313-327页

作者： Bolideh, Ameneh Arab, Hamed Ghohani Ghasemi, Mohammad Reza Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using teaching learning based optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

关键词： continuous beam optimization reinforced concrete teaching learning based optimization algorithm reinforcement detailing cost function

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Position controlling of Sun Tracking System using optimization technique

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ENERGY REPORTS 2020年 6卷 304-309页

作者： Rawat, Anuj Jha, S. K. Kumar, Bhavnesh Netaji Subhas Inst Technol New Delhi India Netaji Subhas Univ Technol NSIT New Delhi India

The prime objective of this paper is to devise optimization technique for the position control of Sun Tracking System (STS). In order to control the position of this system, PID controller tuned by different methods such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and teaching learning based optimization (TLBO) is used. These methods have been carried out to determine the most optimized and robust one for the position control. The simulated results for nominal and perturbed value of servo amplifier gain show that the PID controller tuned with TLBO technique gives most optimized and robust performance in comparison to other tuning techniques. (C) 2019 Published by Elsevier Ltd.

关键词： Sun-Tracking System teaching learning based optimization algorithm Sun axis Solar panel axis

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Location of Distributed Generation in the Distribution Network concerning of Capacity Credit with the TLBO optimization algorithm 32

Location of Distributed Generation in the Distribution Netwo...

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32nd International Conference on Electrical Engineering (ICEE)

作者： Arash, Mohammadali Khakroei, Mohammad MAPNA Elect & Control Engn Mfg Co MECO Alborz Iran

ISBN: (纸本)9798350376357;9798350376340

Electrical energy distribution networks have had many developments in recent years. The advances in distributed generation (D.G) technology are also increasing in the presence of sensitive loads that require high reliability in this network. This article has attempted to capacity credit (C.C) as a new issue in Electrical energy distribution by using D.G sources. The D.G studied in this article was considered wind sources. The problem of planning D.G in the distribution network was modeled as a non-linear optimization. The objective function includes increasing the reliability, reducing loss, and improving the capacity credit of wind-dispersed generation sources. The problem model also considers the technical and the network's economic constraints and D.G's economic constraints. In the planning process, the uncertainties of the network load and the generation power of wind sources were modeled by combining two methods, Monte Carlo and k-means. The cut-set was also used to evaluate the reliability of the network. For the efficiency of the proposed response, the IEEE 33-bus distribution network was studied under two scenarios using the teaching learning-based optimization algorithm. The article's results showed that D.G can create capacity credit up to 33% of the network load.

关键词： Dispersed generation sources capacity credit load planning generation planning teaching learning based optimization algorithm

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Position controlling of Sun Tracking System using optimization technique

Position controlling of Sun Tracking System using optimizati...

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6th International Conference on Power and Energy Systems Engineering (CPESE)

作者： Rawat, Anuj Jha, S. K. Kumar, Bhavnesh Netaji Subhas Inst Technol New Delhi India Netaji Subhas Univ Technol NSIT New Delhi India

关键词： Sun-Tracking System teaching learning based optimization algorithm Sun axis Solar panel axis

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Proposed algorithm (PEA) for optimization 8

Proposed Algorithm (PEA) for Optimization

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8th IEEE International Conference on Cloud Computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Kannojiya, Ravindra Dulwani, Mohnish Sharma, Kartikey Amity Inst Engn & Technol AUUP Noida India

ISBN: (纸本)9781538617199

This study presents comparison of methods to compute optimum value of input parameter to optimize given objective function. In this study objective functions are a basic thermal system and 5 benchmark function. Comparison is made between T.L.B.O. algorithm (teaching learning based optimization algorithm, a nature inspired evolutionary algorithm) and P.E.A. (Planetary Extinction algorithm, a proposed algorithm). Results are concluded by testing both algorithms on thermal system and on 5 benchmark function. In this study thermal system is a basic steam power plant system consisting of boiler, turbine, condenser, pump (neglected because it does not impart much work). Study suggests PEA performed better than TLBO in 5 out of 6 test performed.

关键词： Nature Inspired Evolutionary algorithm teaching learning based optimization algorithm planetary extinction algorithm thermal system power Plant

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