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New improved metaheuristic approaches for optimum design of posttensioned axially symmetric cylindrical reinforced concrete walls

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2018年第7期27卷 e1461-e1461页

作者： Bekdas, Gebrail Istanbul Univ Dept Civil Engn TR-34320 Istanbul Turkey

Two novel hybrid approaches are presented for optimum design of axially symmetric cylindrical walls subjected to posttensioning loads using metaheuristic algorithms such as harmony search (HS), flower pollination algorithm (FPA), and teaching learning based optimization (TLBO). The objective function of the optimization problem is to minimize the total cost of the wall subjected to constraints on the basis of sectional capacities (bending moment, shear force, and axial tension), ACI 318 (building code requirements for structural concrete) requirements and design variables such as wall thickness, compressive strength of concrete, location and intensities of posttensioning cables, size, and spacing of reinforcement. In the optimum design, the performance of the iterative population based metaheuristic algorithms, HS, FPA, and TLBO are compared and tested by taking wall thickness as discrete and continuous variable. In order to improve the efficiency on finding global optimum results, hybrid forms of the HS combined with FPA and TLBO are effective for the optimization problem.

关键词： axially symmetric cylindrical reinforced concrete walls flower pollination algorithm harmony search optimization optimum design teaching learning based optimization

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Optimal planning of distributed energy resources in harmonics polluted distribution system

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 39卷 99-113页

作者： Kumawat, Manoj Gupta, Nitin Jain, Naveen Bansal, R. C. MNIT Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India MPUAT CTAE Dept Elect Engn Udaipur 313001 India Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

In this study, harmonics related to power quality issue and energy demand growth are considered simultaneously towards the realistic planning of medium voltage radial distribution system. Mostly, harmonics of distribution networks are produced in the presence of non-linear loads. Therefore, Distributed Energy Resource (DERs) can be placed to mitigate the harmonic distortions and to supply the required system energy demand. This paper presents a Modified Group experience of teaching learning based optimization approach, which can deal with allocation of DERs efficaciously in distorted and non-distorted radial distribution networks. The effectiveness of the proposed approach is validated on standards 33-bus and 69-bus test systems along with 83-bus (Taiwan Power Company) practical radial non-distorted distribution system. The results are compared with already well-established existing methods as suggested in the literature. Further, the proposed algorithm is applied to DER planning considering harmonics generating loads in above-mentioned test systems. The results with linear as well as non-linear loads on all three test systems prove that the proposed strategy can be a robust approach to enhance the system performance towards mitigating increased load demand within the constraints of the distribution system.

关键词： Distributed energy resources Distribution load flow Harmonics spectrum Power quality teaching learning based optimization

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Coupled flexural torsional analysis and buckling optimization of variable stiffness thin-walled composite beams

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2022年第19期29卷 2795-2815页

作者： Mukherjee, Paulomi Punera, Devesh Mishra, Mayank Indian Inst Technol Bhubaneswar Sch Infrastruct Argul 752050 Khordha India Indian Inst Technol Madras Dept Appl Mech Madras Tamil Nadu India

Present work examines flexural-torsional coupling for static and stability analysis of thin-walled composite beams having variable stiffness. Variable stiffness composites (VSCs), manufactured with curvilinear fibers, are considered in this work. Compared to straight fiber laminates, variable angle tow (VAT) sections may significantly improve structural response as they redistribute the in-plane stresses. A generalized beam kinematic model which considers the effect of flexural, torsional and, warping deformation is adopted and using the principle of minimum potential energy, governing equations are derived. The variable fiber angles can be defined using a general Lagrangian polynomial and as a particular case, the solution is presented for linearly varying fiber angles. Stability analysis considers geometric nonlinearity. The present model neglects transverse shear deformations, and governing equations are numerically solved using a set of C (1) continuous finite elements. Important observations are made on the coupled response of thin-walled VSCs for static and stability analysis, which indicates the necessity of optimization study. An efficient 'teaching learning based optimization' (TLBO) algorithm is applied to obtain the most efficient curvilinear fiber path. The results show that lateral torsional buckling load capacity can be increased by approximately 15% in optimized VSCs compared to unidirectional fiber composites. The present kinematic model and numerical scheme can be easily extended to the optimization studies considering manufacturing constraints, which may be further implemented in commercial designs.

关键词： Flexural– torsional lateral buckling teaching learning based optimization thin-walled variable stiffness

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Wrapper-based Feature Selection for Medical Diagnosis: The BTLBO-KNN Algorithm

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IEEE ACCESS 2023年 11卷 61368-61389页

作者： Seghir, Fateh Drif, Ahlem Selmani, Saadeddine Cherifi, Hocine Ferhat Abbas Univ Setif 1 Lab Syst Intelligents LSI Setif 19000 Algeria Ferhat Abbas Univ Setif 1 Fac Sci Comp Sci Dept Setif 19000 Algeria Univ Burgundy Lab Interdisciplinaire Carnot Bourgogne ICB UMR 6303 CNRS F-21078 Dijon France

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary teaching-learning based optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner's replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.

关键词： Binary optimization algorithm classification feature selection K-nearest neighbor machine learning medical diagnosis teaching learning based optimization

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A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

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Journal of Modern Power Systems and Clean Energy 2014年第4期2卷 298-307页

作者： Zhile YANG Kang LI Qun NIU Yusheng XUE Aoife FOLEY School of Electrical Electronics and Computer ScienceQueen’s University BelfastBelfast BT95AHUK Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200072China NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China School of Mechanical and Aerospace Engineering Queen’s UniversityBelfast BT95AHUK

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing *** two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate *** expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging *** this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch ***-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch *** results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

关键词： Economic dispatch Environmental dispatch Plug-in electric vehicle Self-learning teaching learning based optimization Peak charging Off-peak charging Stochastic charging

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Novel multiobjective TLBO algorithms for the feature subset selection problem

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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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Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第1期6卷 1-12页

作者： Sahu, Neelesh Kumar Andhare, Atul B. Shri Ramdeobaba Coll Engn & Management Dept Ind Engn Nagpur 440013 Maharashtra India Visvesvaraya Natl Inst Technol Dept Mech Engn Nagpur 440010 Maharashtra India

This paper explores use of teaching learning based optimization (TLBO), 'JAYA' (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both -roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, 'JAYA' and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and 'JAYA' algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and 'JAYA' performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： Titanium alloys Response surface methodology teaching learning based optimization 'JAYA' Cutting force Surface roughness

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Novel multi objective evolutionary framework for solving next release problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 3315-3339页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Islamic Azad Univ Dept Comp Engn Yasooj Branch Yasuj Iran Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Dept Elect Engn Yasooj Branch Yasuj Iran

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the nonrepetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

关键词： Next release problem multi-objective evolutionary algorithm search-based software engineering teaching learning based optimization non-repetitive population

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A Tabu Search hyper-heuristic strategy for t-way test suite generation

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APPLIED SOFT COMPUTING 2016年 44卷 57-74页

作者： Zamli, Kamal Z. Alkazemi, Basem Y. Kendall, Graham Univ Malaysia Pahang Fac Comp Syst & Software Engn IBM Ctr Excellence Kuantan 26300 Pahang Darul Ma Malaysia Umm Al Qura Univ Coll Comp & Informat Syst Mecca Saudi Arabia Univ Nottingham Malaysia Campus Sch Comp Sci Jalan Broga Semenyih 43500 Selangor Darul Malaysia

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of teaching learning based optimization, Global Neighborhood Algorithm, Particle Swarm optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Software testing t-way Testing Hype-heuristic Particle Swarm optimization Cuckoo Search Algorithm teaching learning based optimization Global Neighborhood Algorithm

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Heuristic Model to Compute Indices for Classification of Incidence and Non-Incidence of Thunderstorms Over Ranchi With Atmospheric Parameter

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IEEE ACCESS 2021年 9卷 127086-127101页

作者： Bala, Kanchan Paul, Sanchita Ghosh, Mili Birla Inst Technol Mesra Deptt Comp Sci & Engn Ranchi 835215 Bihar India Birla Inst Technol Mesra Dept Remote Sensing Ranchi 835215 Bihar India

Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the teaching learning based optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.

关键词： Mathematical model Storms Atmospheric modeling Monsoons Indexes Meteorology Lightning Correlation coefficient thunderstorm teaching learning based optimization differential evolution simulated annealing

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