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Dexterous workspace optimization for a six degree-of-freedom parallel manipulator based on surrogate-assisted constrained differential evolution

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APPLIED SOFT COMPUTING 2023年 139卷

作者： Pu, Huayan Cheng, Hao Wang, Gang Ma, Jie Zhao, Jinglei Bai, Ruqing Luo, Jun Yi, Jin Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

Parallel manipulators are increasingly applied because of their high stiffness, load capacity, and motion precision. However, the workspace of a parallel manipulator is relatively small compared to that of a serial manipulator. Therefore, appropriate structural designs of parallel manipulators for maximizing the workspace are essential. This paper studies the dexterous workspace maximization problem of a six-degree-of-freedom (DOF) parallel manipulator. First, a constrained optimization model is formulated, in which, the objective is to maximize the dexterous workspace, and the constraints include several kinematic and dynamic indicators, and some general structural limitations. To address the highly constrained, computationally-expensive problem, a surrogate-assisted two-phase constrained differential evolution method is proposed. Its effectiveness and efficiency are verified by the superior performance over two surrogate-free evolutionary methods: differential evolution (DE) and genetic algorithm (GA), and two surrogate-assisted state-of-the-art evolutionary methods: mod-ified constrained expected improvement (CEI) and the lower confidence bounding approach (PCLCB), on ten benchmark functions and the established real-word 6-DOF manipulator design optimization problem. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Work space optimization Parallel manipulator Constrained optimization surrogate-assisted evolutionary algorithm Differential evolution

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surrogate-assisted optimisation design of composite riser

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2016年第1期230卷 18-34页

作者： Wang, Chunguang Shankar, Krishna Ashraf, Muhammad A. Morozov, Evgeny V. Ray, Tapabrata Shandong Univ Technol Sch Architecture Engn Dept Civil Engn 12 Zhangzhou Rd Zibo 255049 Peoples R China Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT Australia

The risers made out of advanced composite materials would be significantly lighter than their metallic counterparts. In addition, they would also offer better fatigue and corrosion resistance. Their use can lead to increased production capacities, reduced operational costs of existing offshore platforms and the possibility of extraction of oil and gas from greater depths. The use of advanced composite materials also offers the possibility of tailoring composite riser's design to meet specific load requirements. In the first phase of this research, the optimisation of the parameters of the composite tubing was attempted through a process of parametric analysis, manual inspection and selection. In the current paper, the optimisation of the composite riser for minimum weight is accomplished through implementation of a more rigorous mathematical optimisation technique. A population-based surrogate-assisted evolutionary algorithm is employed to identify the optimum design. The objective is to achieve the design with minimum structural weight while satisfying critical load cases, both local and global. The approach and the results of the design optimisation of the composite riser are presented. The optimal design results are verified using finite element analysis under local and global design requirements and compared with different design methods.

关键词： surrogate-assisted evolutionary algorithm design optimisation composite riser offshore engineering finite element modelling

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A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion

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MEMETIC COMPUTING 2022年第2期14卷 179-191页

作者： Song, Zhenshou Wang, Handing Xu, Hongbin Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China

surrogate-assisted many-objective optimization is to locate Pareto optimal solutions using a limited number of function evaluations. Most existing surrogate-assisted evolutionary algorithms are designed to embed in a specific many-objective evolutionary algorithm. The Pareto-based bi-indicator infill sampling criterion has been proven to be effective in saving expensive evaluations in surrogate-assisted multi-objective evolutionary algorithms. It introduces two indicators measuring the convergence and diversity as two optimization objectives. In this paper, we extend our previous work to propose a generic framework with a Pareto-based bi-indicator infill sampling criterion for expensive many-objective optimization. The proposed framework gives a general method for traditional MOEAs to solve the expensive optimization task. We incorporate the proposed framework into the reference vectors guided evolutionary algorithm and compare it with another surrogate-assisted reference vectors guided evolutionary algorithm. Empirical studies on DTLZ problems with more than three objectives demonstrate the effectiveness and superiority of the proposed framework and the incorporated algorithm.

关键词： Pareto-based bi-indicatior infill sampling criterion surrogate-assisted evolutionary algorithm Expensive many-objective optimization

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A surrogate-assisted differential evolution for expensive constrained optimization problems involving mixed-integer variables

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INFORMATION SCIENCES 2023年第1期622卷 282-302页

作者： Liu, Yuanhao Yang, Zan Xu, Danyang Qiu, Haobo Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Nanchang Univ Sch Adv Mfg Nanchang 330031 Peoples R China Natl Ctr Technol Innovat Intelligent Design & Nume Wuhan 430074 Peoples R China

Many surrogate-assisted evolutionary algorithms (SAEAs) have been developed for expen-sive constrained optimization problems (ECOPs) with continuous variables. However, there exist some ECOPs that contain mixed-integer variables in real world engineering applica-tions. The mixed-integer variables make the feasible regions of ECOPs exhibit complex dis-joint characteristics, which brings two challenges for the existing methods, i.e., the classical surrogate-assisted pre-screening may easily fall into a local optimum and the population diversity tends to reduce with the influence of integer variables. For tackling these prob-lems, a surrogate-assisted differential evolution for solving ECOPs involving mixed -integer variables (SADE-MI) is proposed. Firstly, the mutation strategy DE/best/2 is used to balance the local and global characteristics of the candidate solutions. Secondly, an adaptive pre-screening operation where the information of the population is effectively employed to construct the distance constraints is designed to avoid excessive exploitation in local regions. Finally, by selecting diversely-located individuals and limiting the number of individuals with the same integer variables, a population diversity maintenance opera-tion is introduced to strengthen global exploration. Experimental studies on some bench-mark problems and engineering optimization cases demonstrate that SADE-MI outperforms other classical algorithms for solving ECOPs with mixed-integer variables.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Adaptive pre-screening operation Differential evolution Expensive constrained optimization problems Mixed-integer Population diversity maintenance operation surrogate-assisted evolutionary algorithm

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Semisupervised Approach to surrogate-assisted Multiobjective Kernel Intuitionistic Fuzzy Clustering algorithm for Color Image Segmentation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第6期28卷 1023-1034页

作者： Zhao, Feng Zeng, Zhe Liu, Hanqiang Lan, Rong Fan, Jiulun Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710061 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710121 Peoples R China

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering algorithms for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering ((SMKIFC)-M-3) algorithm is proposed in this article. The main contributions of (SMKIFC)-M-3 can be summarized as follows: 1) semisupervised kernel intuitionistic fuzzy objective functions are constructed for optimization to search satisfactory segmentation results;2) to reduce the computational cost, the Kriging model is used to predict the values of objective functions instead of directly calculating the expensive objective functions;3) a semisupervised selection strategy and a semisupervised model management mechanism are proposed to balance the convergence and diversity and improve the predicted accuracy of the Kriging model, respectively;and 4) a novel semisupervised kernel intuitionistic fuzzy cluster validity index is defined to select the optimal solution from the final nondominated solution set. Experimental results on two color image libraries demonstrate that (SMKIFC)-M-3 outperforms state-of-the-art methods in segmentation performance and meanwhile possesses a low time cost.

关键词： Clustering algorithms Linear programming Image segmentation Kernel Color evolutionary computation Computational modeling Color image segmentation intuitionistic fuzzy clustering kernel metric kriging model multiobjective optimization semisupervised mechanism surrogate-assisted evolutionary algorithm

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Reliability-enhanced surrogate-assisted particle swarm optimization for feature selection and hyperparameter optimization in landslide displacement prediction

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5417-5447页

作者： Wang, Yi Wang, Kanqi Zhang, Maosheng Gu, Tianfeng Zhang, Hui Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Xi An Jiao Tong Univ Inst Disaster Prevent & Ecol Restorat Xian 710054 Peoples R China Northwest Univ Dept Geol Xian 710127 Peoples R China

Landslides are dangerous disasters that are affected by many factors. Neural networks can be used to fit complex observations and predict landslide displacement. However, hyperparameters have a great impact on neural networks, and each evaluation of a hyperparameter requires the construction of a corresponding model and the evaluation of the accuracy of the hyperparameter on the test set. Thus, the evaluation of hyperparameters requires a large amount of time. In addition, not all features are positive factors for predicting landslide displacement, so it is necessary to remove useless and redundant features through feature selection. Although the accuracy of wrapper-based feature selection is higher, it also requires considerable evaluation time. Therefore, in this paper, reliability-enhanced surrogate-assisted particle swarm optimization (RESAPSO), which uses the surrogate model to reduce the number of evaluations and combines PSO with the powerful global optimization ability to simultaneously search the hyperparameters in the long short-term memory (LSTM) neural network and the feature set for predicting landslide displacement is proposed. Specifically, multiple surrogate models are utilized simultaneously, and a Bayesian evaluation strategy is designed to integrate the predictive fitness of multiple surrogate models. To mitigate the influence of an imprecise surrogate model, an intuitional fuzzy set is used to represent individual information. To balance the exploration and development of the algorithm, intuition-fuzzy multiattribute decision-making is used to select the best and most uncertain individuals from the population for updating the surrogate model. The experiments were carried out in CEC2015 and CEC2017. In the experiment, RESAPSO is compared with several well-known and recently proposed SAEAs and verified for its effectiveness and advancement in terms of accuracy, convergence speed, and stability, with the Friedman test ranking first. For the landslid

关键词： Computationally expensive problems surrogate-assisted evolutionary algorithm Particle swarm optimization Hyperparameter optimization Feature selection

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A Kriging-assisted evolutionary algorithm with multiple infill sampling for expensive many-objective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Zhu, Qingling Kang, Gaoli Wu, Xunfeng Lin, Qiuzhen Tang, Huimei Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

surrogate -assisted evolutionary algorithms (SAEAs) have been extensively used to solve computationally expensive multi -objective optimization problems (MOPs) as they can obtain a set of satisfyingly optimal solutions while remaining within a limited computational budget. Nevertheless, the expensive MOPs with more than three objectives have received little attention, and most existing SAEAs fail to achieve satisfactory results when solving them. Therefore, to fill this research gap, a Kriging-assisted evolutionary algorithm with multiple infill sampling for solving expensive many -objective optimization problems is proposed. In this paper, to balance exploration and exploitation, a new environmental selection operator is proposed, which is composed of three procedures conducted consecutively. In addition, a new multiple infill sampling strategy is proposed to select the most representative solutions for real function evaluations and model updates. Furthermore, to limit the computational costs of constructing/updating the surrogate model, a new archive update strategy is proposed to maneuver the training data set. In experiments, our method is verified on some benchmark problems. The experimental results demonstrate that the proposed algorithm shows promising performance when compared with four state-of-the-art SAEAs for solving expensive many -objective optimization problems.

关键词： surrogate-assisted evolutionary algorithm Kriging model Infill sampling Expensive many-objective optimization

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Transfer learning based surrogate assisted evolutionary bi-objective optimization for objectives with different evaluation times

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KNOWLEDGE-BASED SYSTEMS 2021年 227卷 107190-107190页

作者： Wang, Xilu Jin, Yaochu Schmitt, Sebastian Olhofer, Markus Allmendinger, Richard Univ Surrey Dept Comp Sci Nat Inspired Comp & Engn Guildford Surrey England Honda Res Inst Europe GmbH Carl Legien Str 30 D-63073 Offenbach Germany Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB Lancs England

Various multiobjective optimization algorithms have been proposed with a common assumption that the evaluation of each objective function takes the same period of time. Little attention has been paid to more general and realistic optimization scenarios where different objectives are evaluated by different computer simulations or physical experiments with different time complexities (latencies) and only a very limited number of function evaluations is allowed for the slow objective. In this work, we investigate benchmark scenarios with two objectives. We propose a transfer learning scheme within a surrogate-assisted evolutionary algorithm framework to augment the training data for the surrogate for the slow objective function by transferring knowledge from the fast one. Specifically, a hybrid domain adaptation method aligning the second-order statistics and marginal distributions across domains is introduced to generate promising samples in the decision space according to the search experience of the fast one. A Gaussian process model based co-training method is adopted to predict the value of the slow objective and those having a high confidence level are selected as the augmented synthetic training data, thereby enhancing the approximation quality of the surrogate of the slow objective. Our experimental results demonstrate that the proposed algorithm outperforms existing surrogate and non-surrogate-assisted delay-handling methods on a range of bi-objective optimization problems. The approach is also more robust to varying levels of latency and correlation between the objectives. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Bi-objective optimization Different evaluation times Transfer learning Domain adaptation Co-training surrogate-assisted evolutionary algorithm Bayesian optimizations

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A data augmentation based Kriging-assisted reference vector guided evolutionary algorithm for expensive dynamic multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2022年第0期75卷

作者： Liu, Zhening Wang, Handing Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Quantum Informat Shaanxi Pr Xian 710071 Peoples R China

Expensive dynamic multi-objective optimization problems (EXDMOPs) involve multiple objective functions changing over time steps. In this kind of problems, only a small number of function evaluations can be allowed in each time step. The challenge of EXDMOP is how to quickly and accurately track the changing optimal solutions with only a small number of function evaluations. Although EXDMOP can be solved as a series of independent expensive multi-objective optimization problems or as an ordinary dynamic multi-objective optimization problems, these two methods either ignore the connection between different time steps or degrade the performance significantly due to the requirement for a large number of function evaluations. In this paper, we incorporate two proposed strategies named training data augmentation and sampling pool expansion to the Kriging-assisted reference vector guided evolutionary algorithm to construct a novel algorithm for solving EXDMOPs. The training data augmentation strategy tries to look for the previous similar time step, and adds the solutions evaluated by the objective functions at that time step to current time step. The accuracies of the Kriging models are improved in this way. The sampling pool expansion strategy sends the predicted optimal solutions to the competition of the function evaluations assignment at each time step, where the predicted optimal solutions are generated by the improved population prediction strategy. We set the ablation experiment for the proposed algorithm and compare it with four dynamic multi-objective evolutionary algorithms on various of problems. The experiment results validate the effectiveness of the proposed two strategies. It also indicates that the proposed algorithm is promising for solving EXDMOPs.

关键词： Expensive dynamic multi-objective optimization evolutionary algorithm Kriging models Prediction surrogate-assisted evolutionary algorithm

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Accelerating surrogate assisted evolutionary algorithms for expensive multi-objective optimization via explainable machine learning

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SWARM AND evolutionary COMPUTATION 2024年 88卷

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin East China Normal Univ Shanghai Inst AI Educ Shanghai 200062 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Dept Space Microwave Remote Sensing Syst Beijing 100090 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100039 Peoples R China Shanghai Frontiers Sci Ctr Mol Intelligent Synth Shanghai Peoples R China Lingnan Univ Sch Data Sci Hong Kong Peoples R China Univ Birmingham Sch Comp Sci Birmingham England

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multiobjective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based surrogate-assisted evolutionary algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the fitness values. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the synthetic benchmark problems and three real -world problems involving six state -of -the -art algorithms demonstrate the effectiveness of the proposed framework.

关键词： Explainable machine learning Crossover operator Expensive optimization Multi-objective optimization surrogate-assisted evolutionary algorithm

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