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Novel multiobjective TLBO algorithms for the feature subset selection problem

NEUROCOMPUTING 2018年 306卷 94-107页

作者： Kiziloz, Hakan Ezgi Deniz, Ayca Dokeroglu, Tansel Cosar, Ahmet Turkish Aeronaut Assoc Univ Comp Engn Dept Ankara Turkey Middle East Tech Univ Comp Engn Dept Ankara Turkey

teaching learning based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. In this study, we propose a set of novel multiobjective TLBO algorithms combined with supervised machine learning techniques for the solution of Feature Subset Selection (FSS) in Binary Classification Problems (FSS-BCP). Selecting the minimum number of features while not compromising the accuracy of the results in FSS-BCP is a multiobjective optimization problem. We propose TLBO as a FSS mechanism and utilize its algorithm-specific parameterless concept that does not require any parameters to be tuned during the optimization. Most of the classical metaheuristics such as Genetic and Particle Swarm optimization algorithms need additional efforts for tuning their parameters (crossover ratio, mutation ratio, velocity of particle, inertia weight, etc.), which may have an adverse influence on their performance. Comprehensive experiments are carried out on the well-known machine learning datasets of UCI Machine learning Repository and significant improvements have been observed when the proposed multiobjective TLBO algorithms are compared with state-of-the-art NSGA-II, Particle Swarm optimization, Tabu Search, Greedy Search, and Scatter Search algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： teaching learning based optimization Multiobjective feature selection Supervised learning

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optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

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MATERIALS RESEARCH EXPRESS 2019年第10期6卷

作者： Stalin, B. Kumar, P. Ramesh Ravichandran, M. Kumar, M. Siva Meignanamoorthy, M. Anna Univ Dept Mech Engn Reg Campus Madurai Madurai 625019 Tamil Nadu India K Ramakrishnan Coll Engn Dept Mech Engn Tiruchirappalli 621112 Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Mech Engn Chennai 600062 Tamil Nadu India Mother Terasa Coll Engn & Technol Dept Mech Engn Pudukkottai Tamil Nadu India

The current research work aims to synthesis AA6063-Si3N4 composites by stir casting technique and analyzing their wear behavior using optimization techniques. The selected composition of the proposed composites are AA6063, AA6063-5 wt% Si3N4 and AA6063-10 wt% Si3N4. The Taguchi grey relational analysis (TGA) was used to analyze the wear performance of the synthesized composites. Further, using the measured experimental data, an artificial neural network (ANN) model was developed to predict the wear rate and teaching learning based optimization (TLBO) algorithm was implemented to obtain the optimized parameters value with the objective of minimizing the wear rate. In the existing work, the multiple linear regression model (MLRM) was developed using the measured values of both dependent (response) and independent (process parameter) variables and these models are used in the optimization technique to optimize the process parameters. From the TGA technique a minimum wear rate of 0.0002 mm(3) min(-1) was obtained for 10% wt reinforcement, 29.43N load, 3 m s(-1) is sliding velocity and 1500 mm sliding distance and from the ANN model and TLBO algorithm, it is probable to attain 0.000183 mm(3) min(-1) wear rate for the process parameter values of 9.892% of wt reinforcement, 9.837N of load, 2.936 m s(-1) sliding velocity and 1130.7 mm sliding distance. As compared to TGA method, an amount of 8.4% improvement in wear rate is achieved by implementing ANN-TLBO method.

关键词： composite aluminium optimization wear artificial neural network Taguchi grey relational analysis teaching learning based optimization

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Swarm Intelligence Algorithm for Microwave Filter optimization 6th

Swarm Intelligence Algorithm for Microwave Filter Optimizati...

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6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA)

作者： Chahrazad, Erredir Bouarroudj, Emir Riabi, Mohamed Lahdi Univ Bros Mentouri Constantine 1 Lab Electromagnet & Telecommun Constantine Algeria

ISBN: (纸本)9783319897431;9783319897424

In this paper, three recent swarm intelligence algorithms (spider monkey optimization (SMO), social spider optimization (SSO) and teaching learning based optimization (TLBO)) are proposed to the optimization of microwave filter (H-plane three-cavity filter). The results of convergence and optimization use of these algorithms are compared with the results of the most popular swarm intelligences algorithm, namely particle swarm optimization (PSO) for different common parameters (population size and maximum number of iteration). The results showed validation of the proposed algorithms.

关键词： Swarm intelligence Spider monkey optimization Social spider optimization teaching learning based optimization Particle swarm optimization Microwave filter

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Multi-objective optimization for energy management of fuel cell hybrid electric vehicles

Multi-objective optimization for energy management of fuel c...

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American Control Conference

作者： Liu, Hao Chen, Jian Wu, Chengshuai Chen, Hao Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781538654286

Energy management strategies (EMSs) are crucial for enhancing the economy and durability of fuel cell hybrid electric vehicles (FCHEVs). However, most existing EMSs are only concerned with economy and ignore the durability, especially the durability of the energy sources. In this paper, based on an improved FCHEV powertrain model, an equivalent cost function considering both economy and durability is developed, and then a fuzzy logic based EMS is designend to realize the energy distribution between fuel cell and battery. After that, based on three different driving cycles, the teaching learning based optimization (TLBO) is introduced to solve the parameters optimization problem of the fuzzy logic based multi objective EMS. Test results show that the TLBO algorithm has faster convergence rate and better optimization results than the existing genetic algorithm approach.

关键词： Energy management strategy multi-objective optimization teaching learning based optimization fuel cell hybrid electric vehicles

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Forecasting of rehabilitation treatment in sufferers from lateral displacement of patella using artificial intelligence

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Sport Sciences for Health 2018年第1期14卷 37-45页

作者： Karimzadehfini, Atiye Mahdavinejad, Reza Zolaktaf, Vahid Vahdatpour, Babak Department of Sports Injuries and Corrective Exercises University of Isfahan Isfahan Iran Department of Physical Medicine and Rehabilitation Isfahan University of Medical Sciences Isfahan Iran

Objectives: In this research, the application of artificial intelligence methods for data analysis named hybrid artificial neural network (ANN) with teaching learning based optimization (TLBO) algorithm to predict of the rehabilitation treatment for females with lateral displacement of the patella (LDP) is demonstrated. Methods: The prediction abilities offered using ANN-TLBO model was presented using available data from 48 female patients referred to physical medicine and rehabilitation clinics of Isfahan Ayatollah Kashani medical center and Al Zahra hospital, Iran. In this modeling, clinical characteristics [weight, height, body mass index (BMI), the degree of LDP, affected side and severity of pain] and demographic characteristic (age) were utilized as the input parameters, while the rehabilitation treatment was the output parameter. Results and discussion: The results indicate a high level of efficient of ANN-TLBO model used with an accuracy level of more than 86%. Therefore, this model can be used successfully for the prediction of rehabilitation treatment for females with LDP. © 2017, Springer-Verlag Italia S.r.l.

关键词： Artificial neural network Lateral displacement of patella Rehabilitation treatment teaching learning based optimization

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Intelligent Overcurrent and Distance Relays Coordination:A Comparative Analysis using GA,PSO and TLBO 4

Intelligent Overcurrent and Distance Relays Coordination:A C...

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4th International Conference on Electrical Energy Systems (ICEES)

作者： Roy, Saptarshi Babu, P. Suresh Babu, N. V. Phanendra NIT Warangal EED Hanamkonda Telangana India

ISBN: (纸本)9781538636954

Relay coordination is important for maintaining proper power system operation and control. Relays should be organized in a special way such that every relay should have a backup and coordination time interval (CTI) between primary and back up and different zones of the relays should be maintained. The relays should operate in minimum desirable time satisfying all the co-ordination constraints optimally. Heuristic techniques are frequently used to get optimal solution of this kind of problems. The problem is solved using teaching learning based optimization(TLBO). The results are compared with the results obtained by Genetic Algorithm(GA) and Particle Swarm optimization(PSO) on a 6bus system. Selection of intelligent over current relay characteristics are incorporated to get the desired results in this work. The results obtained from TLBO are found better than the same obtained from GA and PSO.

关键词： Coordination of relay Coordination time interval teaching learning based optimization Time setting multiplier intelligent relay characteristics Convergence characteristics GA PSO

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An Intelligent Approach for Protection Coordination of Overcurrent Relay in Smartgrid

An Intelligent Approach for Protection Coordination of Overc...

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IEEE International Conference on Energy, Systems and Information Processing

作者： Papia Ray Debani Prasad Mishra Sabha Raj Arya Electrical Engineering Department VSSUT Electrical Engineering Department IIIT Electrical Engineering Department SVNIT

ISBN: (纸本)9781728104201

For smooth operation of the micro grid, optimal setting of overcurrent relay is required. In this paper optimal settings of overcurrent relays have been carried out by teaching learning-based optimization (TLBO) to reduce the incidence of sympathetic trippings when the transient network topology takes place during faults and only relay and circuit breakers of one end have operated for the clearing of fault. The work considers identification of sympathetic tripping and shows that it is possible to obtain relay settings which are unaffected by transient network topology changes. It is observed from simulation work that the coordination time interval (CTI) may be adversely affected if all sympathetic and probable sympathetic tripping are removed.

关键词： Over current relay Micro grid teaching learning based optimization Sympathetic tripping Coordination time interval overcurrent relay Network topology smooth operation relay faults

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Forecasting gas density using artificial intelligence

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PETROLEUM SCIENCE AND TECHNOLOGY 2017年第9期35卷 903-909页

作者： Choubineh, Abouzar Khalafi, Elias Kharrat, Riyaz Bahreini, Alireza Hosseini, Amir Hossein Petr Univ Technol Petr Dept Ahvaz Iran Shiraz Univ Petr Dept Shiraz Iran Semnan Univ Petr Dept Semnan Iran

Proper calculations of gas engineering require precise determination of gas properties and its associated variations with pressure and temperature. These properties can be determined by conducting experimental tests on gathered fluid samples from the bottom of the wellbore or at the surface as well as using equations of state and empirical correlations. This work is concentrated to develop a robust and quick model based on artificial network trained with teaching learning based optimization (ANN-TLBO) using 693 data sets at a wide range of pressure and temperature for gas density prediction. Comparing gas density from the predictive method and experimental results describe that the proposed ANN-TLBO model is of reliable accuracy for determining gas density. Sensitivity analysis also showed the extreme effect of temperature and pressure on gas density.

关键词： Artificial neural network comparing gas density sensitivity analysis teaching learning based optimization

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Hybrid algorithm DE-TLBO for optimal H-a and PID control for multi-machine power system

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2017年第2期8卷 925-936页

作者： Dib, Faiza Boumhidi, Ismail Univ Sidi Mohamed Ben Abdellah LESSI Lab Fac Sci Dhar El Mehraz Fes Morocco

This paper propose, a robust excitation controller designed by a coordination of the optimal H-a tracking control and the proportional integral derivative (PID) controller optimized by the hybrid differential evolution and teaching-learning based optimization algorithm (DE-TLBO). These two controllers are used in order to guarantee the transient stability during a change in the operating conditions and the uncertainties in parameters. We have applied a method based on the modified tracking error by using the optimized exponential function, to avoid the compromise between the high gain in the control input and the H-infinity tracking performance with the variation in the system parameter. A new hybrid algorithm (DE-TLBO) is employed in this study to adjust optimally the parameters of the (PID-PSS) controller and the exponential form of the tracking error modified. The purpose of the suggested approach is to ensure a good tracking accuracy and to enhance the level of the oscillations damping in the multi-machine power system with an optimal choice of the parameters of all proposed controllers. The results of simulation demonstrate the efficient, and the robustness of the proposed approach (H-infinity and DE-TLBO-PID-PSS) under the different operation conditions.

关键词： Differential evolution teaching learning based optimization H-infinity tracking control Multi-machine power system Proportional integral derivative

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Transmission Congestion Management in Restructured Power System by Rescheduling of Generators Using TLBO

Transmission Congestion Management in Restructured Power Sys...

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IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE)

作者： Bhattacharya, Sayantan Kuanr, Biswa Ranjan Routray, Aliva Dash, Abhishek Jadavpur Univ Power Engn Dept Kolkata W Bengal India VSSUT Elect Engn Dept Burla India Natl Inst Technol Elect Engn Dept Kozhikode Kerala India

ISBN: (纸本)9781509049967

Congestion management in transmission line is one of the most difficult chores faced by system operator in the rising restructured power system. Congestion in transmission line creates problem for smooth operation in restructured electricity market and increases transmission cost, which leads to profit minimization of the electric power seller and decreases the market efficiency. One of the most effective techniques to get rid of congestion is active power rescheduling of generators. The primary objective of this work is to minimize the rescheduling cost while simultaneously managing the congestion. For optimization of rescheduling cost one of the most recently developed meta-heuristic technique teaching learning based optimization (TLBO) is employed. In order to establish the usefulness of the proposed methodology it is applied on the standard IEEE 30 bus system to manage three different congestion scenarios at optimal cost. Results thus obtained are found to be qualitatively better than some other standard optimization techniques.

关键词： Generator rescheduling Congestion management Generator sensitivity factor Restructured power system teaching learning based optimization

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