Article
Computer Science, Artificial Intelligence
Jinglu Li, Peng Wang, Huachao Dong, Jiangtao Shen, Caihua Chen
Summary: In this article, a classification surrogate-assisted multi-objective evolutionary algorithm (CSA-MOEA) is proposed for solving expensive optimization problems. The algorithm adopts a classification tree as the surrogate model and obtains valuable solutions through two infilling strategies.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann
Summary: A new surrogate-assisted evolutionary algorithm is proposed in this study to handle expensive irregular multi-objective optimization problems. The algorithm balances convergence and diversity by adapting reference vectors and implementing a surrogate management strategy, effectively taking irregularity of the Pareto front into account.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jiangtao Shen, Peng Wang, Ye Tian, Huachao Dong
Summary: This paper proposes a dual surrogate-assisted evolutionary algorithm (DSAEP-PS) based on parallel search to solve multi-objective and computationally expensive simulations in the real world. The algorithm combines approximation and classification models, introduces parallel search and strengthened dominance relation to improve optimization performance, and demonstrates its effectiveness on benchmark problems and engineering applications.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yi Zhao, Chaoli Sun, Jianchao Zeng, Ying Tan, Guochen Zhang
Summary: This paper proposes a method to train multiple surrogate models to assist many-objective optimization algorithm for solving expensive many-objective problems. Experimental results show that this method is competitive with other surrogate-assisted evolutionary algorithms within a limited computational budget.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Dong Han, Wenli Du, Xinjie Wang, Wei Du
Summary: The study introduces a surrogate-assisted decomposition-based evolutionary algorithm that considers the balance between exploration and exploitation by incorporating distribution information of weight vectors and population, as well as develops a replacement strategy to limit model training time. Empirical results demonstrate competitive performance of the algorithm, with superior outcomes when applied to real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi Zhao, Jianchao Zeng, Ying Tan
Summary: The proposed method combines reference vector guided evolutionary algorithm and radial basis function networks to optimize individuals and introduces an infill strategy, showing competitive performance in solving computationally expensive many-objective optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Xiaoyue Zhang, Lu Chen, Naixue Xiong
Summary: This study proposes an improved Bagging ensemble surrogate-assisted evolutionary algorithm (IBE-CSEA) to address the issues in solving expensive many-objective optimization problems. The algorithm uses an ensemble classifier to classify offspring, selects boundary individuals for training and testing, and ultimately selects promising individuals for evaluation based on classification results. Compared with current popular surrogate-assisted evolutionary algorithms, IBE-CSEA algorithm demonstrates superior competitiveness.
APPLIED INTELLIGENCE
(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, 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
Computer Science, Information Systems
Ruoyu Wang, Yuee Zhou, Hanning Chen, Lianbo Ma, Meng Zheng
Summary: This paper proposes a surrogate-assisted many-objective evolutionary algorithm that utilizes the cooperation of multi-classification and regression models to improve search quality and reduce computational cost. It divides the population into classes using a multi-classification model for diversity, and utilizes distance and angle regression models for convergence and diversity in each class. Experimental results confirm its effectiveness on expensive test problems with up to 10 objectives.
Article
Computer Science, Artificial Intelligence
Maoqing Zhang, Lei Wang, Weian Guo, Wuzhao Li, Junwei Pang, Jun Min, Hanwei Liu, Qidi Wu
Summary: This paper introduces a dominance degree metric to enhance the comparability of non-dominated solutions in many-objective optimization problems. Based on this metric, a novel Many-Objective Evolutionary Algorithm is proposed, showing superior performance in terms of convergence, diversity, and spread compared to other state-of-the-art optimizers.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Wenxin Wang, Huachao Dong, Peng Wang, Jiangtao Shen
Summary: This paper proposes a bi-indicator-based surrogate-assisted evolutionary algorithm (BISAEA) for solving computationally expensive multi-objective optimization problems (MOPs). BISAEA utilizes a Pareto-based bi-indicator strategy and a radius-based function (RBF) model to approximate objective values. It also incorporates a one-by-one selection strategy based on angles and Pareto dominance to improve diversity. Experimental results show that BISAEA achieves high efficiency and a good balance between convergence and diversity. Application of BISAEA to a multidisciplinary optimization problem further demonstrates its superior performance on computationally expensive engineering problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jinglu Li, Peng Wang, Huachao Dong, Jiangtao Shen
Summary: This paper presents a two-stage surrogate-assisted evolutionary algorithm (TS-SAEA) for computationally expensive multi/many-objective optimization. The algorithm consists of a convergence stage and a diversity stage, which effectively optimize the objective space. Experimental results show that TS-SAEA has significant advantages on multi/many-objective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Ye Tian, Jiaxing Hu, Cheng He, Haiping Ma, Limiao Zhang, Xingyi Zhang
Summary: In this paper, a novel surrogate-assisted evolutionary algorithm is proposed, which employs a surrogate model to conduct pairwise comparisons between candidate solutions instead of directly predicting solutions' fitness values. Compared to regression and classification models, the proposed model based on pairwise comparison can better balance between positive and negative samples, and be directly used, reversely used, or ignored based on its reliability in model management. The experimental results on abundant benchmark and real-world problems demonstrate that the proposed surrogate model is more accurate and outperforms state-of-the-art surrogate models.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Zhenshou Song, Handing Wang, Cheng He, Yaochu Jin
Summary: The proposed algorithm uses Kriging-assisted two-archive EA for expensive many-objective optimization, employing an influential point-insensitive model to approximate each objective function and proposing an adaptive infill criterion for determining an appropriate sampling strategy. Experimental results have shown its superiority over five state-of-the-art SAEAs on a set of expensive multi/many-objective test problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Meirong Chen, Yinan Guo, Yaochu Jin, Shengxiang Yang, Dunwei Gong, Zekuan Yu
Summary: This study proposes an environment-driven hybrid dynamic multi-objective evolutionary optimization method to balance the quality of obtained solutions and the computation cost, and select an appropriate optimization method based on the characteristics of the dynamic environment.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yan Zhou, Yaochu Jin, Yao Sun, Jinliang Ding
Summary: Liquid state machines (LSMs) are biologically more plausible than spiking neural networks for brain-inspired computing and neuromorphic engineering. However, optimizing and training complex recurrent network architectures in LSMs is challenging. This paper proposes a generative LSM with evolved reservoir architecture and optimized weights through a cooperative co-evolutionary algorithm and synaptic plasticity rules. Experimental results on benchmark problems show that the proposed algorithm outperforms state-of-the-art methods, and the data parallelism strategy effectively speeds up the evaluation process.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Shangshang Yang, Haoyu Wei, Haiping Ma, Ye Tian, Xingyi Zhang, Yunbo Cao, Yaochu Jin
Summary: In this paper, a personalized exercise group assembly (PEGA) method is proposed to assemble personalized exercise groups based on students' abilities for flexible exercise recommendations. Experimental results demonstrate that the assembled exercises by the proposed method are more effective in enhancing students' proficiency on both poorly mastered and new knowledge concepts compared to existing exercise recommendation methods.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Nian Zhang, Zhigang Zeng, Yaochu Jin
Summary: This special issue presents robust, explainable, and efficient next-generation deep learning algorithms with data privacy and theoretical guarantees to improve the understanding and explainability of deep neural networks; improve the accuracy of deep learning leveraging new stochastic optimization and neural architecture search; and increase the computational efficiency and stability of the deep learning training process with new algorithms that will scale.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xi Zhang, Guo Yu, Yaochu Jin, Feng Qian
Summary: This paper proposes a transfer learning based surrogate assisted evolutionary algorithm (TrSA-DMOEA) to efficiently solve expensive dynamic multi-objective optimization problems (EDMOPs). By utilizing the knowledge from previous high-quality solutions, Gaussian process models are built to improve the computational complexity and solution quality. Furthermore, an adaptive acquisition function based surrogate-assisted mechanism is introduced to balance convergence and diversity. Experimental results demonstrate the superiority of the proposed method in solving EDMOPs.
Article
Computer Science, Artificial Intelligence
Jia Liu, Ran Cheng, Yaochu Jin
Summary: This paper proposes a bi-fidelity multiobjective neural architecture search approach to enhance the adversarial robustness of deep neural networks. The approach formulates the neural architecture search problem as a multiobjective optimization problem and reduces the computational cost by combining three performance estimation methods. Extensive experiments on different datasets validate the effectiveness of the proposed approach.
Article
Computer Science, Artificial Intelligence
Xueming Yan, Yaochu Jin, Xiaohua Ke, Zhifeng Hao
Summary: Multi-echelon location-routing problems (ME-LRPs) are challenging due to uncertain assignment relationship and increasing number of echelons. This study utilizes a hierarchical fuzzy graph and evolutionary multi-tasking optimization algorithm to solve ME-LRPs. The proposed method considers multi-echelon assignment information and routing task selection to alleviate negative transfer between different tasks, and demonstrates competitiveness on benchmark problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xilu Wang, Yaochu Jin
Summary: In this paper, a personalized evolutionary Bayesian algorithm is proposed to handle optimization problems with personalized variables. This algorithm considers personalized information and measurement noise by using a contextual Gaussian process and an evolutionary algorithm.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Hai-Feng Zhang, Xiao-Jing Ma, Jing Wang, Xingyi Zhang, Donghui Pan, Kai Zhong
Summary: This study proposes a method based on secure multiparty computation to integrate the information of multiple private networks and improve link prediction performance without violating data privacy agreement.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuanchao Liu, Jianchang Liu, Yaochu Jin, Fei Li, Tianzi Zheng
Summary: This work proposes a novel surrogate-assisted two-stage differential evolution (SA-TSDE) algorithm for expensive constrained optimization. It combines a hybrid differential evolution with a repair strategy in the first stage, and a clustering strategy with local surrogates in the second stage. Experimental results show that SA-TSDE is highly competitive compared with state-of-the-art methods.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianfeng Qiu, Shengda Shu, Qiangqiang Zhang, Chao Wang, Fan Cheng, Xingyi Zhang
Summary: In this paper, a robust evolutionary optimization approach named REO is proposed to enhance the robustness of existing multi-objective evolutionary algorithms (MOEAs) for maximizing ROC convex hull. By designing a distance-based samples selection method and a problem-oriented two-stage adaptive updating strategy, the robustness of MOEAs is improved. Experimental results demonstrate the effectiveness of the proposed REO approach in enhancing the robustness of MOEAs for ROC convex hull maximization under label noise.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Rongsheng Wang, Qi Zhang, Xuewu Dai, Zhiming Yuan, Tao Zhang, Shuxin Ding, Yaochu Jin
Summary: This paper investigates the HSTR problem under a partial station blockage and proposes an efficient PS-SEGA algorithm for solving it. The algorithm utilizes permutation-based encoding and heuristic decoding methods to find an optimized rescheduled timetable. Additionally, a hybrid initialization method and restart strategy are presented to enhance the algorithm's performance.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Shuangming Yang, Yanwei Pang, Haowen Wang, Tao Lei, Jing Pan, Jian Wang, Yaochu Jin
Summary: In this article, a novel biologically plausible learning method is proposed to address the challenge of designing an efficient learning mechanism with spiking dendrites. The method utilizes a multi-scale learning rule with dendritic predictive characteristics and a two-phase learning mechanism based on burst-related plateau potential dynamics. Experimental results show that the proposed algorithm improves learning accuracy, reduces synaptic operations, and power consumption on neuromorphic hardware, while enhancing robustness and learning convergence speed.
Article
Computer Science, Artificial Intelligence
Zhun Fan, Zhaojun Wang, Wenji Li, Xiaomin Zhu, Bingliang Hu, An-Min Zou, Weidong Bao, Minqiang Gu, Zhifeng Hao, Yaochu Jin
Summary: Swarm robotic systems (SRSs) are used in various fields, and designing local interaction rules for self-organization of robots is a challenging task. This study proposes a modular design automation framework for gene regulatory network (GRN) models that can generate entrapping patterns without the need for expertise. The framework utilizes basic network motifs and multi-objective genetic programming to optimize the structures and parameters of the GRN models. Simulation results show that the framework can generate novel GRN models with simpler structures and better performance in complex environments. Proof-of-concept experiments using e-puck robots validate the feasibility and effectiveness of the proposed GRN models.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Meng Wu, Xiaomin Zhu, Li Ma, Weidong Bao, Zhun Fan, Yaochu Jin
Summary: Multi-robot systems outperform single robots in accomplishing challenging tasks due to their properties that single robots lack. This paper proposes a cooperative hierarchical gene regulatory network (CH-GRN) to enhance mutual cooperation between robots and utilize obstacles for more effective target entrapment. The CH-GRN includes a target-neighbour-obstacle (TNO) pattern generation method and a concentration-vector method for adaptation and obstacle avoidance. Simulation experiments and Kilobot experiments demonstrate the effectiveness of the CH-GRN in various challenging environments with different types of obstacles.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
