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An Efficient Hybrid Particle Swarm and teaching-learning-based optimization for Arch-Dam Shape Design

STRUCTURAL ENGINEERING INTERNATIONAL 2023年第4期33卷 640-658页

作者： Shahrouzi, Mohsen Naserifar, Yaser Kharazmi Univ Fac Engn Civil Engn Dept Tehran Iran

Particle swarm optimization is a popular meta-heuristic with highly explorative features;however, in its standard form it suffers from a poor convergence rate and weak search refinement on multi-dimensional problems. The present work improves the conventional particle swarm optimizer in three ways: adding a greedy selection for better intensification;embedding an extra movement borrowed from teacher-learner-based optimization;and utilizing a neighborhood strategy by averaging over a random half of the swarm. The performance of the proposed method is subsequently evaluated on three sets of problems. The first set includes uni-modal, multi-model, separable and non-separable test functions. The proposed method is compared with a standard particle swarm optimizer and its variants as well as other meta-heuristic algorithms. Engineering benchmark problems including the optimal design of a tubular column, a coiled spring, a pressure vessel and a cantilever beam constitute the second set. The third set includes constrained sizing design of a 120-bar dome truss and the optimal shape design of the Morrow Point double-arch concrete dam as a practical case study. Numerical results reveal considerable enhancement of the standard particle swarm via the proposed method to exhibit competitive performance with the other studied meta-heuristics. In the optimal design of Morrow Point Dam, the proposed method resulted in a material consumption 21 times smaller than the best of the initial population and 26% better than a recommended practical design.

关键词： Computational intelligence particle swarm optimizer teaching-learning-based optimization constrained engineering problem dam shape design

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WOA-TLBO: Whale optimization algorithm with teaching-learning-based optimization for global optimization and facial emotion recognition

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APPLIED SOFT COMPUTING 2021年 110卷 107623-107623页

作者： Lakshmi, A. Vijaya Mohanaiah, P. JNTUA Dept ECE Anantapur AP India NBKR Inst Sci & Technol Dept ECE Vijayanagar India

The Whale optimization Algorithm (WOA) is a recently developed algorithm that is based on the chasing mechanism of humpback whales. Benefiting from the unique structure, WOA has virtuous global search capability. One of the drawbacks of this algorithm is the slow convergence rate that limits its real-world application. In resolving complicated global optimization problems, without any exertion for adequate fine-tuning preliminary constraints, teaching-learning-based optimization (TLBO) is smooth to plunge into local optimal, but it has a fast convergence speed. By given the features of WOA and TLBO, an active hybrid WOA-TLBO algorithm is proposed for resolving optimization difficulties. To explore the enactment of the proposed WOA-TLBO algorithm, several experimentations are accompanied by regular benchmark test functions and compared with six other algorithms. The investigational outcomes indicate the more magnificent concert of the proposed WOA-TLBO algorithm for the benchmark function results. The proposed method has also been applied to the Facial Emotion Recognition (FER) functional problem. FER is the thought-provoking investigation zone that empowers us to classify the expression of the human face in everyday life. Centered on the portions' actions in the human face, the maximum of the standard approaches fail to distinguish the expressions precisely as the expressions. In this paper, we have proposed FER's productive process using WOA-TLBO based MultiSVNN (Multi-Support Vector Neural Network). Investigational outcomes deliver an indication of the virtuous enactment of the proposed technique resolutions in terms of accurateness. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic Whale optimization algorithm WOA-TLBO Facial emotion recognition Population-based algorithm teaching-learning-based optimization

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Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Fatehi, Mohammad Toloei, Alireza Zio, Enrico Niaki, S. T. A. Keshtegar, Behrooz Shahid Beheshti Univ Fac New Technol & Aerosp Engn Tehran Iran PSL Univ MINES Paris Paris France Politecn Milan Energy Dept Milan Italy Sharif Univ Technol Dept Ind Engn Tehran Iran Univ Zabol Fac Engn Dept Civil Engn Zabol Iran

This research proposes a novel approach for the robust optimization of the design of hydrogen peroxide propulsion control systems using the efficient and advanced teaching-learning-based optimization (TLBO) method. This study adopts a robust design optimization (RDO) formulation that considers both epistemic and aleatory uncertainties, including sparse points and interval data, and uses the Johnson distribution family for uncertainty representation. The maximum likelihood estimation method is applied to determine the distribution parameters, also considering interval data with a nested optimization technique. A novel advanced TLBO method with high accuracy and convergence rate is employed to optimization of this robust design approach. The method's originality and advancement come from two categories of modifications to the original framework: the structure of the teaching and learning phases and the initialization and search approach. The efficacy and applicability of the proposed Ad-TLBO, respectively, were evaluated using benchmark problems from the CEC2020 competition and three real-world engineering problems, with a comparison to some recently published and the CEC competition's top-ranked algorithms. The results and statistical analyses of the Quade test, Wilcoxon signed-rank test, and Friedman test show that the proposed Ad-TLBO method outperforms the other algorithms. The proposed optimization method is eventually applied to design a monopropellant propulsion system as the control actuator of a satellite orbital transfer system. It is found that the proposed advanced TLBO is effective in handling uncertainty in real design problems and improves both the convergence rate and accuracy of the optimization process.

关键词： Robust design teaching-learning-based optimization Aleatory uncertainty Epistemic uncertainty Monopropellant propulsion system Satellite orbital transfer system

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Modified teaching-learning-based optimization and applications in multi-response machining processes

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 174卷

作者： Ang, Koon Meng Natarajan, Elango Sharma, Abhishek Rahman, Hameedur Then, Richie Yi Shiun Alrifaey, Moath Tiang, Sew Sun Lim, Wei Hong Isa, Nor Ashidi Mat UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus George Town 14300 Malaysia Ariel Univ Dept Elect & Elect Engn IL-40700 Ariel Israel Univ Petr & Energy Studies Dept Elect & Elect Engn Dehra Dun 248007 India Air Univ Fac Comp & Artificial Intelligence Islamabad Capital Territo 44000 Pakistan

Many real-world engineering problems such as machining processes are multi-objective optimization problems (MOPs) because multiple performance characteristics are considered to satisfy their contradictory goals. An improved multi-objective teaching-learning-based optimization with refined knowledge sharing mechanisms (IMTLBO-RKSM) is proposed to tackle these MOPs effectively. Pareto dominance concept is first incorporated into IMTLBO-RKSM to handle the tradeoffs of multiple contradictory objectives. Appropriate modifications are incorporated into both teacher and learner phases of IMTLBO-RKSM to emulate to emulate the knowledge sharing processes of classroom more accurately, hence achieving better balancing of exploration and exploitation searches. Particularly, both concepts of Euclidean-distance based teacher assignment scheme and social learning are incorporated into the IMTLBO-RKSM's teacher phase to derive the unique directional information that can provide better guidance for each learner. The learner phase of IMTLBO-RKSM is also modified by designing two new learning mechanisms known as independent learning and adaptive peer learning, aiming to facilitate different preferences of learners in acquiring new knowledge. The performance of IMTLBO-RKSM is evaluated and compared with six multi-objective optimization methods by using five case studies of multi-response machining problems and twelve MOP benchmark functions. Extensive simulation studies show that IMTLBORKSM have more competitive performance than other methods by generating Pareto fronts with better quality in terms of accuracy and diversity of solution members for most tested problems.

关键词： Multi-objective optimization Multi-response machining Pareto dominance teaching-learning-based optimization

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A modified teaching-learning-based optimization algorithm for solving optimization problem

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KNOWLEDGE-based SYSTEMS 2021年 212卷 106599-106599页

作者： Ma, Yunpeng Zhang, Xinxin Song, Jiancai Chen, Lei Tianjin Univ Commerce Sch Informat Engn CO-300134 Tianjin Peoples R China

In order to reduce the NOx emissions concentration of a circulation fluidized bed boiler, a modified teaching-learning-based optimization algorithm (MTLBO) is proposed, which introduces a new population group mechanism into the conventional teaching-learning based optimization algorithm. The MTLBO still has two phases: teaching phase and learning phase. In teaching phase, all students are divided into two groups based on the mean marks of the class, the two groups present different solution updating strategies, separately. In learning phase, all students are divided into two groups again, where the first group includes the top half of the students and the second group contains the remaining students. The two groups also have different solution updating strategies. Performance of the proposed MTLBO algorithm is evaluated by 14 unconstrained numerical functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed. In addition, the tuned extreme learning machine by MTLBO is applied to establish the NOx emission model. based on the established model, the MTLBO is used to optimize the operation conditions of a 330 MW circulation fluidized bed boiler for reducing the NOx emissions concentration. Experimental results reveal that the MTLBO is an effective tool for reducing the NOx emissions concentration. (C) 2020 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Modified teaching-learning-based optimization Extreme learning machine NOx emission model Circulation fluidized bed boiler

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An improved teaching-learning-based optimization for design of frequency reconfigurable antennas

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第12期32卷 e4030-e4030页

作者： Zadehparizi, Fatemeh Jam, Shahrokh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran

This paper proposes an improved teaching-learning-based optimization (ITLBO) for the electromagnetics and antenna community. The basic TLBO was applied to many different problems with consistent success. For improving the algorithm performance, in the learner phase, each group of two learners is replaced by three-member groups. Furthermore, two new phases are added to the algorithm. The first phase involves selection of several better learners for teaching subjects to randomly selected weaker learners. The second phase is a mutation operation. The presented algorithm with these changes simulates a classroom more realistically and avoids being trapped in local optima. First, the ITLBO is evaluated to optimize seven benchmark functions and is shown to be faster and better. In addition, a frequency reconfigurable antenna design is considered as a practical problem and Taguchi's method is applied for adjusting the control parameters of the algorithm. Comparisons of the performance of this algorithm with those of the TLBO, two other improved versions of TLBO (I-TLBO and improved TLBO with learning experience of other learners [LETLBO]) and genetic algorithms demonstrate the higher efficiency for this algorithm in electromagnetic problems. A prototype of the proposed antenna is fabricated, and the simulation and measurement results agree well.

关键词： frequency reconfigurable antennas internal multiport method Taguchi's method teaching-learning-based optimization

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An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Chen, Zhenyu Liu, Yang Yang, Zhile Fu, Xuewei Tan, Jiubin Yang, Xiaofeng Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn Harbin Peoples R China Harbin Inst Technol Minist Ind & Informat Technol Key Lab Ultraprecis Intelligent Instrumentat Harbin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen Peoples R China Fudan Univ Sch Microelect Shanghai Peoples R China

teaching-learning-based optimization (TLBO) is a recently proposed meta-heuristic optimization method and demonstrates outstanding performance for solving numerous sciences and engineering problems. However, many studies have shown that TLBO has a strong bias towards converging to origin and poorly performs in the problems with shifted solutions. To conquer this problem, a novel self-adaptive hybrid self-learning based TLBO (SHSLTLBO) is proposed in this paper. By constructing a self-adaptive framework, the original TLBO is fused with the Gaussian distribution, and the novel updating law switches in two modes corresponding to the fitness during the searching process. Additionally, in order to avoid local convergence in the initialization, a self-learning phase is introduced, associated to the original teaching and learning phase. The performance of the proposed SHSLTLBO is tested in the numerical benchmark functions, where the comprehensive comparison carries out with the state-of-the-art TLBO variants and other meta-heuristic approaches. The results demonstrate superior advantages of the proposed algorithm at balancing along the evolutionary stages among the TLBO variants. In the experiments, the two well-performed meta-heuristic optimization methods, namely LSHADE and HCLPSO, are used as the comparative methods to SHSLTLBO, where their superior performances of them are shown on the shifted problems. With increasing dimensionality, the performance of TLBO goes down rapidly while LSHADE's relative ranking improves. Apart from these scenarios, the comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.

关键词： Meta-heuristic algorithm teaching-learning-based optimization GBTLBO algorithm Self-learning Self-adaptive

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MTLBO: A Multi-objective Multi-course teaching-learning-based optimization Algorithm

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淡江理工学刊 2018年第3期21卷 331-342页

作者： Wenwen Lin Lei Wang Guangdong Tian Yuejun Zhang

This paper presents a multi-objective evolutionary algorithm, which is called multi-course teaching-learning-based optimization algorithm. It consists of four phases: teacher, leaner, teacher training, and exchange student phases. Three novel modifications are introduced into the basic teaching-learning-based optimization algorithm. It helps diversify evolutionary population, enhance stochastic search ability, and avoid premature convergence. Eight widely used benchmark problems are employed in order to investigate the performance of the proposed algorithm. The experimental results indicate that it finds better Pareto optimal solutions than some other state-of-the-art algorithms for the majority of problems.

关键词： teaching-learning-based optimization Multi-objective optimization Evolutionary Algorithm Heuristic Algorithm

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Modified teaching-learning-based optimization Algorithm

Modified Teaching-Learning-Based Optimization Algorithm

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32nd Chinese Control Conference (CCC)

作者： Tuo ShouHeng Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723000 Peoples R China

ISBN: (纸本)9781479900305

In allusion to the shortcoming of global exploration performance of basic teaching-learning-based optimization (TLBO) algorithm in solving complex high-dimensional problems, a modified teaching-learning-based optimization (MTLBO) algorithm is proposed. In MTLBO algorithm, the methods of teaching phase and learning phase are respectively modified to enhance to disturbance potential of search space, and a new "Self-learning" method is presented to enhance the innovation ability of the learner and the global exploration performance. Finally, the performance of the proposed MTLBO algorithm is investigated over 6 complex high-dimensional benchmark functions. The results show that the proposed MTLBO algorithm has some advantages over convergence velocity, accuracy, and stability.

关键词： teaching-learning-based optimization "Self-learning" method complex high-dimensional optimization Problem

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Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures

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KSCE JOURNAL OF CIVIL ENGINEERING 2014年第6期18卷 1759-1767页

作者： Dede, Tayfun Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

The aim of this study is to present a new efficient optimization algorithm called teaching-learning-based optimization (TLBO). The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several benchmark problem related truss structures with discrete design variables are used to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

关键词： teaching-learning-based optimization discrete optimization truss structure weight minimization

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