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Landslide susceptibility modeling based on ANFIS with Teaching-learning-based optimization and Satin bowerbird optimizer

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Geoscience Frontiers 2021年第1期12卷 93-107页

作者： Wei Chen Xi Chen Jianbing Peng Mahdi Panahi Saro Lee College of Geology and Environment Xi’an University of Science and TechnologyXi’an 710054China Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Natural ResourcesXi’an 710021China Department of Geological Engineering Chang’an UniversityXi’an 710054China Division of Science Education College of Education#4-301Gangwondaehak-gil Chuncheon-siKangwon National UniversityGangwon-do 24341South Korea Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132South Korea Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113South Korea

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the Teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and ef

关键词： Landslide susceptibility Step-wise weight assessment ratio analysis Adaptive neuro-fuzzy fuzzy inference system Teaching-learning-based optimization Satin bowerbird optimizer

An improved Teaching-learning-based optimization

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An improved teaching-learning-based optimization

第37届中国控制会议

作者： Jie Hou Ziwu Ren Pan Lu Kunting Zhang Robotics and Microsystems Center Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University

Teaching-learning-based optimization（TLBO） is a new proposed heuristic algorithm for optimization applications in recent years. In this paper, an improved TLBO algorithm（ITLBO） is presented. In the teacher phase, the second-teaching strategy and self-exploration study of teacher are introduced to improve the convergence speed. And the improved learner phase can ensure the diversity of the population to avoid the possibility of falling into a local optimum. Meanwhile, second-teaching strategy and the improved learner phase enable the algorithm to use fine local search and improve the precision. To assess the performance of ITLBO algorithm, experiments are implemented on 8 classical benchmark functions. The result show that ITLBO algorithm is an effective approach.

关键词： Teaching-learning-based optimization Second-teaching strategy Teacher’s self-exploration study

Teaching-learning-based optimization algorithm for hybrid flow shop scheduling problem with assembly operations

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Teaching-learning-based optimization algorithm for hybrid fl...

第30届中国控制与决策会议

作者： Zhiwei Xu Deming Lei School of Automation Wuhan University of Technology

This paper addresses assembly-type hybrid flow shop scheduling problem（AHFSP） with the minimi-zation of total tardiness and makespan. The products have parts which are first processed in hybrid flow shop and then are assembled at the assembly stage. To solve this problem, a part-based solution representation is adopted and a teachers’ Teaching-learning-based optimization（TTLBO） is proposed. Teachers chosen from a non-dominated set undergo a self-learning phase and a teaching phase and these phases are implemented by multiple neighborhood search and global search, respectively. More-over, the learning phase of learners is removed and the learning of teachers is intensified to avoid the low-efficiency computation. Extensive experiments are conducted to test the performance of TTLBO. The computational results demonstrate that TTLBO is an effective and competitive method for multi-objective AHFSP.

关键词： Hybrid flow shop scheduling multi-objective optimization assembly Teaching-learning-based optimization

Hybrid teaching-learning based optimization with Harmony Search for Engineering optimization Problems

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Hybrid Teaching-Learning Based Optimization with Harmony Sea...

第36届中国控制会议

作者： Haibin Ouyang Ge Ma Guiyun Liu Zhifu Li Xiaojing Zhong School of Mechanical and Electric Engineering Guangzhou University

ISBN: (纸本)9781538629185

In order to improve the performance of Teaching-learning-based optimization algorithm, a hybrid Teaching-learning-based optimization algorithm is presented in this paper. The hybrid approach combine with the strength of harmony search algorithm and Teaching-learning-based optimization algorithm, which is aim to enhance the global search ability and local exploitation cababillity. Moreover, A new learning operation is used in learning phase for improving learning efficiency. Several classic engineering cases are selected to evaluate the performance of the proposed algorithm. Results reveal that the proposed algorithm outperforms TLBO and some other promising approachs.

关键词： Teaching-learning-based optimization harmony search algorithm learning operation

ABC-TLBO: A Hybrid Algorithm based on Artificial Bee Colony and Teaching-learning-based optimization

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ABC-TLBO: A Hybrid Algorithm Based on Artificial Bee Colony ...

第37届中国控制会议

作者： Mei Zhang Yuchong Pan Jinhui Zhu Guangsen Chen School of Automation Science and Engineering South China University of Technology School of Software Engineering South China University of Technology

Artificial bee colony（ABC） is shown to be effective in solving optimization problems because of its good exploration capability. However it suffers from poor exploitation because onlooker bees use the same searching method as employed bees. In order to remedy the limited exploitation capability of ABC and achieve a better balance between exploration and exploitation, searching strategies for employed bees and onlooker bees are redesigned respectively and a novel algorithm called ABC-TLBO is proposed. It inherits the framework of basic ABC, and meanwhile an individualized TLBO based on Teaching-learning-based optimization（TLBO） is embedded in onlooker bee phase to improve the exploitation capability of the presented algorithm. Moreover, a new searching strategy for employed bee phase which combines direction guiding searching and differential evolution crossover searching is applied to increase the population diversity and accelerate the convergence. The proposed ABC-TLBO algorithm is validated on benchmark functions and it is compared with ABC-based, TLBO-based and nature-inspired algorithms. Experimental results reveal that ABC-TLBO algorithm is an efficient, fast converging optimization method for solving global optimization problems.

关键词： Artificial bee colony Teaching-learning-based optimization global numerical optimization

基于TLBO算法的不确定性条件下复杂产品协同设计的可靠性拓扑优化

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Engineering 2023年第3期22卷 71-81页

作者： Zhaoxi Hong Xiangyu Jiang 冯毅雄 Qinyu Tian 谭建荣 State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province Zhejiang UniversityHangzhou 310027China

复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑... 详细信息

复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑优化方法。将由板结构形成的支撑物作为拓扑优化对象,重量轻、稳定性好。将不确定性下的可靠性优化和结构拓扑优化协同处理。首先,利用有限差分法将优化问题中的不确定性参数修正为确定性参数。然后,将不确定性可靠性分析和拓扑优化的复杂嵌套解耦。最后,利用TLBO算法求解解耦模型,该算法参数少,求解速度快。TLBO算法采用了自适应教学因子,在初始阶段实现了更快的收敛速度,并在后期进行了更精细的搜索。本文给出了一个液压机基板结构的数值实例,说明了该方法的有效性。

关键词： Plates structure Reliability Collaborative topology optimization Teaching-learning-based optimization algorithm Uncertainty Collaborative design for product life cycle

A Bi-population Cooperative optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction tool.The search operation conducted in this low space facilitates the population with fast convergence towards the optima.To strike the balance between exploration and exploitation during optimization,two phases of a tailored Teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process.Also,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed.The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200.As indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer.Compared with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems Teaching-learning-based optimization