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Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems

APPLIED SOFT COMPUTING 2021年 99卷 106934-106934页

作者： Dong, Huachao Wang, Peng Yu, Xinkai Song, Baowei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

In this work, a surrogate-assisted teaching-learning-based optimization algorithm is presented for high-dimensional and computationally expensive black-box optimization problems. In the presented method, a two-phase searching framework is proposed to realize the exploitation of surrogates and the metaheuristic exploration. Specifically, radial basis functions are used to build the dynamically updated surrogate models. Moreover, a surrogate-assisted knowledge mining strategy is proposed to sufficiently collect valuable information in each cycle. In this strategy, multiple groups of promising points are selected from the expensive sample set to construct the subspaces and local surrogate models, from which the most potential points are captured for the subsequent updates and optimization. At the same time, a population composed of the present best expensive samples carries out the teaching/learning-based search, accelerating local convergence and promoting global exploration. In the surrogate-assisted teaching phase, both of individual and mean behaviors are considered to make learners go close to the teacher efficiently. In the surrogate-assisted learning phase, a more extensive search range is defined to improve sampling diversity. The cooperation of the surrogate-assisted knowledge mining and the modified teaching-learning-based exploration makes the new method have excellent performance on 21 benchmark cases with 30-100 design variables. Furthermore, this method is used for the shape optimization of a blended-wing-body underwater glider, and gets impressive results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Radial Basis Function Computationally expensive High-dimensional teaching-learning-based optimization

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A stigmergic approach to teaching-learning-based optimization for continuous domains

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Meghdadi, Aghdas Akbarzadeh-T, M. R. Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Dept Elect Engn Mashhad Razavi Khorasan Iran

teaching-learning-based optimization (TLBO) has shown considerable success in solving complex optimization problems by imitating the higher cognitive leader-follower interactions among students and teachers in a classroom. Here, we propose to solve function optimization with a stigmergic probabilistic memory structure based on the indirect cooperation and distributed information exchange among students of a class along with a diversity preserving operator. The stigmergic approach integrates the historical experience, while the probabilistic representation and the diversity preserving operators avoid premature convergence. The resulting Stigmergic Real domain TLBO (SRTLBO) is applied to optimization problems with continuous variables. The proposed SRTLBO is evaluated on 40 standard benchmark functions, as well as in modeling the earthquake source and wave propagation in the East of Iran from actual data. Results indicate overall improved solutions of the SRTLBO algorithm when compared to several competing algorithms. In several cases, particularly those functions with a simpler structure, considerable improvement is also observed in the rate of convergence to the optimum solution. Superscript/Subscript Available

关键词： Metaheuristic optimization algorithms Swarm intelligence Stigmergy teaching-learning-based optimization Earthquake ground motion

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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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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 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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Optimized Path Planning for Three-Wheeled Autonomous Robot Using teaching-learning-based optimization Technique 1

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International Conference on Advances in Materials and Manufacturing Engineering (ICAMME)

作者： Kashyap, Abhishek K. Pandey, Anish KIIT Deemed Be Univ Sch Mech Engn Bhubaneswar 751024 India

ISBN: (数字)9789811513077

ISBN: (纸本)9789811513077;9789811513060

Path planning is a leading topic in the field of the wheeled robot (WR). Three basic characteristic path planning should have when the WR is traveling toward the goal: (1) obtain information about the given working space conditions, (2) location of itself, and (3) optimize the decision to reach the target. The current research paper focuses on obtaining an efficient and robust technique to guide the WR. teaching-learning-based optimization technique is the centerpiece of the present research work. Fitness function has been presented to optimize the path planning and reaching target. Parameters selected for the proposed technique are (1) distance between robot, start point, goal, and obstacles and (2) turning angle while avoiding obstacles. The technique is examined in various environments with the different level of difficulties. The WR efficiently reaches the target by avoiding collision with the obstacles. In addition, the proposed technique is compared with the previously used technique. The obtained simulated results justified that the teaching-learning-based optimization technique selects better travel path and have shorter travel length.

关键词： Wheeled robot Path planning Obstacle avoidance teaching-learning-based optimization Static obstacles

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Binary teaching-learning-based optimization algorithm with a new update mechanism for sample subset optimization in software defect prediction

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SOFT COMPUTING 2019年第20期23卷 9919-9935页

作者： Thanh Tung Khuat My Hanh Le Univ Danang Univ Sci & Technol Da Nang Vietnam

Software defect prediction has gained considerable attention in recent years. A broad range of computational methods has been developed for accurate prediction of faulty modules based on code and design metrics. One of the challenges in training classifiers is the highly imbalanced class distribution in available datasets, leading to an undesirable bias in the prediction performance for the minority class. Data sampling is a widespread technique to tackle this problem. However, traditional sampling methods, which depend mainly on random resampling from a given dataset, do not take advantage of useful information available in training sets, such as sample quality and representative instances. To cope with this limitation, evolutionary undersampling methods are usually used for identifying an optimal sample subset for the training dataset. This paper proposes a binary teaching-learning- based optimization algorithm employing a distribution-based solution update rule, namely BTLBOd, to generate a balanced subset of highly valuable examples. This subset is then applied to train a classifier for reliable prediction of potentially defective modules in a software system. Each individual in BTLBOd includes two vectors: a real-valued vector generated by the distribution-based update mechanism, and a binary vector produced from the corresponding real vector by a proposed mapping function. Empirical results showed that the optimal sample subset produced by BTLBOd might ameliorate the classification accuracy of the predictor on highly imbalanced software defect data. Obtained results also demonstrated the superior performance of the proposed sampling method compared to other popular sampling techniques.

关键词： teaching-learning-based optimization Binary teaching-learning-based optimization Distribution-based update Sample subset optimization Imbalanced learning Software defect prediction

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Two-stage teaching-learning-based optimization method for flexible job-shop scheduling under machine breakdown

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第5-8期100卷 1419-1432页

作者： Buddala, Raviteja Mahapatra, Siba Sankar Natl Inst Technol Rourkela Dept Mech Engn Rourkela India

In the real-world situations, uncertain events commonly occur and cause disruption of normal scheduled activities. Consideration of uncertain events during the scheduling process helps the organizations to make strategies for handling the uncertainties in an effective manner. Therefore, in the present paper, unexpected machine breakdowns have been considered during scheduling of jobs in a flexible job-shop environment. The objective is to obtain lowest possible makespan such that robust and stable schedules are produced even if an unexpected machine breakdown occurs. The robust and stable schedules may help to decrease the costs associated with unexpected machine failures. The present work uses a two-stage teaching-learning-based optimization (2S-TLBO) method to solve flexible job-shop scheduling problem (FJSP) under machine breakdown. In the first stage, the primary objective of makespan is optimized without considering any machine breakdown. In the second stage, a bi-objective function considering robustness and stability of the schedule is optimized under uncertainty of machine breakdowns. In order to incorporate the machine breakdown data to basic FJSP, a non-idle time insertion technique is used. In order to generate effective robust and stable predictive FJSP schedules, a rescheduling technique called modified affected operations rescheduling (mAOR) is used. The Kacem's and Brandimarte's benchmark problems have been solved and compared with other algorithms available in the literature. Results indicate that TLBO outperforms other algorithms by generating superior robust and stable predictive schedules. Statistical analysis is carried out to test the significance difference of the results obtained by TLBO with other algorithms.

关键词： Flexible job-shop scheduling Machine breakdown Makespan Meta-heuristics Robustness Stability teaching-learning-based optimization

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Hybrid CNN-GRU Forecasting and Improved teaching-learning-based optimization for Cost-Efficient Microgrid Energy Management

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PROCESSES 2025年第5期13卷 1452-1452页

作者： Alharbi, Mishal Alghamdi, Ali S. Majmaah Univ Coll Engn Dept Elect Engn Al Majmaah 11952 Saudi Arabia

In this paper, a two-stage framework is proposed for the energy management of microgrids, which combines a hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) forecast model and the Improved teaching-learning-based optimization (ITLBO) algorithm. The CNN-GRU model captures spatiotemporal patterns in historical data for effective renewable energy and load demand uncertainty quantification, while the ITLBO algorithm improves generation scheduling performance through utilization of adaptive luminance coefficients, Latin Hypercube initialization, and hybrid genetic operations. The proposed framework is then compared with four different forecasting models: standalone CNN or MLANN, and three popular optimization algorithms (PSO, TLBO, CO) for four cases, including baseline (perfect foresight), CNN-GRU forecast, CNN forecast, and MLANN forecast. The results show that the hybrid framework outperforms dedicated, in-domain models for forecast and scheduling, with the state-of-the-art CNN-GRU sliding window model producing the best forecasting accuracy, which subsequently translates into near-optimal scheduling performance. Through many experiments, we show that the ITLBO algorithm is robust and outperforms the classical optimization methods on convergence speed and solution quality while significantly eliminating the forecast errors uncertainty. Demand response is also a feature of these models, which boosts operational efficiency by scaling down peak grid usage without sacrificing affordability through energy saving capabilities. According to the results, the hybrid framework exhibits significant cost-efficiency by reducing the RMSE of solar irradiance forecasting by 11.6% when compared to standalone CNN and achieving a 69.7% reduction in operational costs under ITLBO optimization. The comparative analysis emphasizes the robustness and versatility of the framework, reinforcing its feasibility across a range of forecasting and optimization scenarios for real-wo

关键词： microgrid optimization renewable energy forecasting hybrid CNN-GRU teaching-learning-based optimization demand response

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Hybridizing teaching-learning-based optimization With Adaptive Grasshopper optimization Algorithm for Abrupt Motion Tracking

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IEEE ACCESS 2019年 7卷 168575-168592页

作者： Zhang, Huanlong Gao, Zeng Ma, Xiaoyang Zhang, Jie Zhang, Jianwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Software Engn Coll Zhengzhou 450002 Peoples R China

Aiming at the problem that conventional tracking algorithms are difficult to deal with abrupt motion efficiently, an optimization algorithm called hybrid teaching-learning-based optimization with Adaptive Grasshopper optimization Algorithm (TLGOA) is proposed in this paper. Firstly, the non-linear strategy based on tangent function is used to replace the linear mechanism in the standard Grasshopper optimization Algorithm (GOA). The improved adaptive GOA (AGOA) can avoid the local trapping problem and enhance the global optimization ability, which can handle the problem of abrupt motion. Secondly, considering that teaching-learning-based optimization (TLBO) has obviously local exploitation operator and fast convergence, a hybrid TLGOA tracker is designed by combining the advantages of both AGOA and TLBO. The approach can enable better tracking accuracy and efficiency. Finally, extensive experimental results show that the proposed algorithm has obvious advantages over other algorithms, and also prove that TLGOA tracker is very competitive compared to other state-of-the-art trackers, especially for abrupt motion tracking.

关键词： Visual tracking abrupt motion grasshopper optimization algorithm teaching-learning-based optimization

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