Article
Computer Science, Artificial Intelligence
Huayan Pu, Hao Cheng, Gang Wang, Jie Ma, Jinglei Zhao, Ruqing Bai, Jun Luo, Jin Yi
Summary: This paper studies the problem of maximizing the dexterous workspace of a six-degree-of-freedom parallel manipulator. A surrogate-assisted two-phase constrained differential evolution method is proposed. Its effectiveness and efficiency are verified through comparisons with other methods on benchmark functions and a real-world application.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Nengxian Liu, Jeng-Shyang Pan, Shu-Chuan Chu, Taotao Lai
Summary: This article introduces an efficient surrogate-assisted bi-swarm evolutionary algorithm (SABEA) with hybrid and ensemble strategies for computationally expensive optimization problems. The proposed SABEA combines differential evolution (DE) and teaching-learning-based optimization (TLBO) to achieve strong exploration and exploitation capabilities. Moreover, the cooperation of global and local surrogate models effectively estimates the fitness value. Experimental results demonstrate the superior performance of SABEA compared to state-of-the-art competing algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Guodong Chen, Yong Li, Kai Zhang, Xiaoming Xue, Jian Wang, Qin Luo, Chuanjin Yao, Jun Yao
Summary: The study proposes a novel and efficient hierarchical surrogate-assisted differential evolution (EHSDE) algorithm for high-dimensional expensive optimization problems. By balancing exploration and exploitation using a hierarchical framework and utilizing global and local surrogate models to accelerate convergence speed, the algorithm demonstrates effectiveness and efficiency on benchmark functions and production optimization problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Mengtian Wu, Lingling Wang, Jin Xu, Pengjie Hu, Pengcheng Xu
Summary: This paper proposes an adaptive technique for improving surrogate models and enhances the search performance of surrogate-assisted evolutionary algorithms by introducing multi-objective evolutionary algorithms as optimizers.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Genghui Li, Zhenkun Wang, Maoguo Gong
Summary: Researchers propose a new algorithm, called SAMFEO, which combines surrogate-assisted and model-free evolutionary optimization to tackle complex and high-dimensional problems. Experimental results demonstrate that SAMFEO outperforms several state-of-the-art methods on complex benchmark problems and a real-world problem.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuanchao Liu, Jianchang Liu, Shubin Tan
Summary: This paper proposes a surrogate-assisted evolutionary algorithm based on decision space partition (DSP-SAEA) for dealing with expensive optimization. The algorithm introduces a two-stage search strategy that integrates global search and local search. In the global search stage, the decision space is partitioned into regions based on clustered evaluated points, where a surrogate model is constructed for each region. The algorithm simultaneously searches these regions with the help of the built surrogate models, resulting in obtaining promising points distributed in different regions. In the local search stage, a model adaptive selection strategy and a trust region local search are integrated. Experimental results demonstrate that DSP-SAEA performs competitively compared with state-of-the-art algorithms on benchmark problems and parameter estimation for frequency-modulated sound waves problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jeng-Shyang Pan, Nengxian Liu, Shu-Chuan Chu, Taotao Lai
Summary: Surrogate-assisted evolutionary algorithms (SAEAs) combine the searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models, and an efficient SAHO algorithm integrates TLBO and DE algorithms, alternating between global exploration and local exploitation when better solutions cannot be found, with a new prescreening criterion selecting promising candidates for evaluations, and using a local RBF surrogate model to mimic the target function landscape.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Xin Zhao, Xue Jia, Tao Zhang, Tianwei Liu, Yahui Cao
Summary: This study proposes a supervised surrogate-assisted evolutionary algorithm (SSAEA) that uses surrogate models to evaluate individual fitness in order to solve complex optimization problems. The SSAEA introduces two novel strategies to improve solution quality and algorithm efficiency. Experimental results demonstrate that the proposed algorithm can obtain higher quality solutions in a shorter computational time.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Yuanhao Liu, Zan Yang, Danyang Xu, Haobo Qiu, Liang Gao
Summary: This paper proposes a surrogate-assisted differential evolution algorithm (SADE-MI) for solving expensive constrained optimization problems with mixed-integer variables. It overcomes the challenges caused by mixed-integer variables through adaptive pre-screening operation and population diversity maintenance operation, and outperforms other classical algorithms in benchmark problems and engineering optimization cases.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yuxing Wang, Tiantian Zhang, Yongzhe Chang, Xueqian Wang, Bin Liang, Bo Yuan
Summary: This paper proposes a generic module called Surrogate-assisted Controller (SC) that can be applied on existing hybrid learning frameworks to alleviate the computational burden of expensive fitness evaluation. The functionality and effectiveness of SC are highlighted through empirical studies.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yufeng Zhou, Xuexian Li, Shunling Ruan
Summary: Model management is crucial for surrogate-assisted evolutionary algorithms to tackle computationally expensive optimization problems. The proposed algorithm in this paper, based on radial space division, outperforms commonly used surrogate-assisted evolutionary algorithms in benchmark and automobile structure design problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Zan Yang, Haobo Qiu, Liang Gao, Danyang Xu, Yuanhao Liu
Summary: This paper proposes a general framework of surrogate-assisted evolutionary algorithms (GF-SAEAs) to adaptively arrange search strategies based on actual simulation cost differences. It classifies all constraints and designs a level-by-level feasible region-driven local search strategy to locate potential sub-feasible regions. Three different search mechanisms are employed to explore and exploit these located regions. Experimental studies show that GF-SAEAs outperform other state-of-the-art algorithms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Hao Chen, Weikun Li, Weicheng Cui
Summary: Fitness functions of real-world optimization problems often require expensive experiments or numerical simulations for analysis. Integrating these into optimization algorithms directly leads to high computational costs. Surrogate-assisted evolutionary algorithms (SAEAs) have gained attention for their high efficiency in solving real world optimization problems. However, with the increase in dimension, the computational cost of constructing surrogates increases and their prediction accuracy may degrade. This paper proposes a surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy (SAEA-HAS) to address these challenges. Experimental results validate the effectiveness of SAEA-HAS.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Neal N. Xiong, Song Jiang, Lu Chen
Summary: A surrogate-assisted evolutionary algorithm is proposed in this paper for solving expensive constrained multi-objective discrete optimization problems. By embedding random forest models and an improved stochastic ranking strategy, the algorithm makes significant progress in optimization efficiency and candidate solution quality.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Xunzhao Yu, Yan Wang, Ling Zhu, Dimitar Filev, Xin Yao
Summary: This article proposes a surrogate-assisted bilevel evolutionary algorithm to solve a real-world engine calibration problem. Principal component analysis is performed to investigate the impact of variables on constraints and to divide decision variables into lower-level and upper-level variables. Computational studies demonstrate that our algorithm is efficient in constraint handling and achieves a smaller fuel consumption value than other calibration methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Cybernetics
Jinghui Zhong, Tiantian Cheng, Wei-Li Liu, Peng Yang, Ying Lin, Jun Zhang
Summary: This article proposes an evolutionary framework to automatically optimize guardrail layouts in subway stations, providing high-quality designs through novel encoding methods and fitness evaluation functions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Jia Wang, Zhi-Hui Zhan, Yun Li, Sam Kwong, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel local search technique, named FDLS, based on individual information including fitness and distance, to execute precise local search operations on global optima in multimodal algorithms, avoiding meaningless local search operations on local optima or similar areas. The proposed FDLS technique is integrated with an adaptive differential evolution algorithm called ADE, and the experiments on the CEC2015 multimodal competition demonstrate its effectiveness and superiority compared to other multimodal algorithms.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhenhao Shuai, Hongbo Liu, Zhaolin Wan, Wei-Jie Yu, Jun Zhang
Summary: This study proposes a self-adaptive neuroevolution (SANE) approach to automatically construct various lightweight DNN architectures for different tasks. SANE is able to self-adaptively adjust evolution exploration and exploitation to improve search efficiency. The results illustrate that the obtained DNN architectures could have smaller scale with similar performance compared to existing DNN architectures.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Zongxi Li, Xianming Li, Haoran Xie, Fu Lee Wang, Mingming Leng, Qing Li, Xiaohui Tao
Summary: Researchers have found that emotion is not limited to one category in emotion-relevant classification tasks, and multiple emotions can exist together in a sentence. Recent studies have focused on using distribution or grayscale labels to enhance the classification model, providing additional information on the intensity of emotions and their correlations. This approach has been effective in overcoming overfitting and improving model robustness. However, it can also reduce the model's discriminative ability within similar emotion categories.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li
Summary: Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs with limited labeled data. However, in extreme cases where very few labels are available (e.g., only 1 labeled node per class), GNNs suffer from severe performance degradation. To address this issue, the proposed Stabilized Self-Training (SST) framework effectively handles the scarcity of labeled data and improves classification accuracy.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jian-Yu Li, Ke-Jing Du, Zhi-Hui Zhan, Hua Wang, Jun Zhang
Summary: This article proposes a novel three-layer DDE framework, along with three novel methods, for solving the resource allocation and search efficiency problems in distributed differential evolution. The effectiveness and efficiency of the framework and methods are demonstrated through theoretical analysis and extensive experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xin Zhang, Bo-Wen Ding, Xin-Xin Xu, Jian-Yu Li, Zhi-Hui Zhan, Pengjiang Qian, Wei Fang, Kuei-Kuei Lai, Jun Zhang
Summary: Decomposition methods are important in CCEAs for solving large-scale optimization problems. The proposed GDD method improves the grouping accuracy by mining interactions among variables and dealing with computational roundoff errors. GDD shows better performance in grouping and fault tolerance, especially for overlapping problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: In this paper, the user route planning problem (URPP) in a bike-sharing system is studied, and a knowledge learning and random pruning-based memetic algorithm (KLRP-MA) is proposed to efficiently solve it. The proposed algorithm not only considers the static perspective of users but also addresses the challenges of the URPP in dynamic situations. Experimental results show that the KLRP-MA and the DyKLRP-MA can find the best solution in a short time, and the DyKLRP-MA can quickly respond to changes and re-optimize the current planning route.
Article
Computer Science, Artificial Intelligence
Junlan Dong, Jinghui Zhong, Wei-Neng Chen, Jun Zhang
Summary: This paper introduces a federated genetic programming framework that can train a global model while protecting data privacy. By processing decentralized data locally without sending the original data to the server, it achieves data privacy protection and reduces data collection time.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xiao-Fang Liu, Zhi-Hui Zhan, Jun Zhang
Summary: This paper proposes an incremental particle swarm optimization method to address the key issues of problem decomposition and solution reconstruction in large-scale dynamic optimization problems. Experimental results demonstrate the superiority of the proposed method in terms of solution optimality.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Jiayuan Xie, Jiali Chen, Wenhao Fang, Yi Cai, Qing Li
Summary: Visual question generation focuses on target objects in images to generate questions for specific questioning purposes. Existing studies extract target objects corresponding to the questioning purpose based on answers. However, answers may not accurately and completely map to every target object. This study proposes a content-controlled question generation model that generates questions based on a given target object set specified from an image.
Article
Automation & Control Systems
Xiao-Qi Guo, Wei-Neng Chen, Feng-Feng Wei, Wen-Tao Mao, Xiao-Min Hu, Jun Zhang
Summary: Surrogate-assisted evolutionary algorithms have been proposed to solve data-driven optimization problems. However, most existing methods do not consider the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. In this study, we propose edge-cloud co-EAs to address distributed data-driven optimization problems, where data are collected by edge servers.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Chemistry, Multidisciplinary
Runze Mao, Wenqi Fan, Qing Li
Summary: Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. Graph condensation has emerged as a promising approach to reduce training cost by compressing large graphs, but its fairness in treating node subgroups during compression has not been explored.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Jiajin Wu, Bo Yang, Runze Mao, Qing Li
Summary: Sequential recommendation systems have gained significant attention, but current models still suffer from popularity bias. To alleviate this bias, this study proposes a debiasing model that considers the dynamic user desire and conducts intervention analysis and counterfactual reasoning. The proposed model, PAUDRec, outperforms existing models while alleviating popularity bias in sequential recommendation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
