Article
Environmental Sciences
Yilin Chen, Bo Gao, Tao Lu, Hui Li, Yiqi Wu, Dejun Zhang, Xiangyun Liao
Summary: This article presents an improved dragonfly algorithm combined with a directed differential operator for feature selection. By adaptively adjusting the step size, designing a new differential operator, and updating the directed differential operator, the proposed method enhances the search capability and convergence speed. Experimental results demonstrate that the proposed algorithm outperforms other representative algorithms in terms of both convergence speed and solution quality.
Article
Computer Science, Artificial Intelligence
Linqiang Pan, Wenting Xu, Lianghao Li, Cheng He, Ran Cheng
Summary: A rotation-based simulated binary crossover (RSBX) method is proposed to improve the performance of multi-objective evolutionary algorithms on problems with rotated Pareto sets. By introducing rotation property and an adaptive selection strategy, both SBX and RSBX are utilized simultaneously.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yupeng Han, Hu Peng, Changrong Mei, Lianglin Cao, Changshou Deng, Hui Wang, Zhijian Wu
Summary: This paper proposes a new multistrategy multiobjective differential evolutionary algorithm, RLMMDE, to solve the exploration and exploitation dilemma in multiobjective optimization problems (MOPs). The algorithm utilizes a multistrategy and multicrossover DE optimizer, an adaptive reference point activation mechanism based on RL, and a reference point adaptation method. Experimental results show that RLMMDE outperforms some advanced MOEAs on benchmark test suites and practical mixed-variable optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Naili Luo, Wu Lin, Genmiao Jin, Changkun Jiang, Jianyong Chen
Summary: In this paper, a novel genetically hybrid differential evolution strategy (GHDE) for recombination in MOEA/Ds is proposed to enhance search capability by introducing two composite operator pools. Through adaptive parameter tuning and fitness-rate-rank-based multiarmed bandit (FRRMAB), the best operator pool is selected, demonstrating the superiority of MOEA/D-GHDE in multiobjective optimization problems.
Article
Computer Science, Artificial Intelligence
Fangqing Gu, Haosen Liu, Yiu-ming Cheung, Hai -Lin Liu
Summary: This study proposes an adaptive constraint regulation method to balance the feasibility and convergence of solutions by adjusting the constraint violation of infeasible solutions. Experimental results demonstrate that the proposed method effectively achieves solution balance and improves solution diversity.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Junzhong Ji, Tongxuan Wu, Cuicui Yang
Summary: The article proposes a multimodal multiobjective differential evolution algorithm with species conservation to locate different Pareto-optimal solution sets (PSs) in known areas and explore new areas for diverse solutions. Comparative experiments have shown that the proposed algorithm performs competitively on multimodal multiobjective optimization problems (MMOPs).
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yuxuan Luan, Junjiang He, Jingmin Yang, Xiaolong Lan, Geying Yang
Summary: This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address the common challenge faced by many existing algorithms in solving real-world optimization problems. By employing strategies such as uniform initialization and self-organizing map, the algorithm improves the population diversity and uniformity. Comparative analysis with 13 other algorithms validates the superiority of the proposed algorithm in terms of uniformity and objective function balance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Wu Lin, Zexuan Zhu, Maoguo Gong, Jianqiang Li, Carlos A. Coello Coello
Summary: This article proposes a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces to maintain diversity in solutions. Experimental results validate the advantages of this approach in maintaining diversity in both objective and decision spaces.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Shuai Wang, Aimin Zhou, Bingdong Li, Peng Yang
Summary: The article proposes a new differential evolution algorithm (RMDE) that improves the search performance for multiobjective optimization problems by sampling guiding solutions from regularity models.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, Carlos A. Coello Coello
Summary: This paper proposes the definition of the biparty multiobjective optimal power flow (BPMOOPF) problem and introduces a novel evolutionary biparty multiobjective optimization algorithm (BPMOOPF-EA) to solve the problem. Experimental results show that BPMOOPF-EA outperforms other algorithms in solving the MOOPF problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Yingbo Xie, Junfei Qiao, Ding Wang, Baocai Yin
Summary: The paper proposes a novel multiobjective optimization evolutionary algorithm, MOEA/D-IMA, based on improved adaptive dynamic selection strategies and elite archive strategy to enhance population diversity and convergence; experimental results show that MOEA/D-IMA significantly improves optimization performance when dealing with MOPs.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Tianqi Gao, Hao Li, Maoguo Gong, Mingyang Zhang, Wenyuan Qiao
Summary: This paper proposes a novel efficient metaheuristic change detection procedure based on superpixel-based multiobjective optimization. The method improves the accuracy of change detection by modeling a multiobjective optimization problem and designing a new mutation operator. Experimental results on real SAR datasets demonstrate the effectiveness of the proposed method in change detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shijie Xiong, Wenyin Gong, Kai Wang
Summary: This paper proposes an enhanced adaptive neighborhood-based speciation differential evolution (EANSDE) algorithm to solve multimodal optimization problems (MMOPs). The algorithm adaptively controls parameters to alleviate the fine-tuning process by users. It introduces an external archive to store inferior solutions and merges them with the current population in the following search. Additionally, a crowding relieving mechanism is proposed to remove extremely similar individuals from the population. Experimental results demonstrate the superiority of EANSDE on the 20 benchmark MMOPs in CEC-2013.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Naili Luo, Yulong Ye, Wu Lin, Qiuzhen Lin, Victor C. M. Leung
Summary: A novel multimodal multiobjective memetic algorithm is proposed in this paper, which preserves more global and local Pareto optimal solution sets using a local detection mechanism and a clustering-based selection strategy. Experimental results demonstrate the superior performance of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Zhengping Liang, Xiuju Xu, Ling Liu, Yaofeng Tu, Zexuan Zhu
Summary: This article proposes an evolutionary many-task optimization algorithm, EMaTO-MKT, based on a multisource knowledge transfer mechanism. The algorithm adaptively determines the probability of using knowledge transfer and balances the self-evolution and knowledge transfer among tasks. It selects multiple highly similar tasks as learning sources and applies a knowledge transfer strategy based on local distribution estimation. Experimental results show the competitiveness of EMaTO-MKT in solving many-task optimization problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Zhengping Liang, Tiancheng Wu, Xiaoliang Ma, Zexuan Zhu, Shengxiang Yang
Summary: In recent years, dynamic multiobjective optimization problems (DMOPs) have gained increasing attention. This article proposes a dynamic multiobjective evolutionary algorithm (DMOEA-DVC) based on decision variable classification, aiming to balance population diversity and convergence. Experimental results comparing DMOEA-DVC with six other algorithms on 33 benchmark DMOPs demonstrate its superior overall performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yuchao Su, Naili Luo, Qiuzhen Lin, Xia Li
Summary: Multiobjective optimization is crucial in practical engineering applications, but becomes more challenging with increased number of objectives. This paper proposes a many-objective immune algorithm that utilizes global information to select high-quality parents for evolution, enhancing convergence and diversity of the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an ensemble surrogate-based framework for solving computationally expensive multiobjective optimization problems (EMOPs). The framework trains a global surrogate model and multiple surrogate submodels to enhance prediction accuracy and reliability. Experimental results demonstrate the advantages of this approach in solving EMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zengyang Shao, Lijia Ma, Yuan Bai, Shanfeng Wang, Qiuzhen Lin, Jianqiang Li
Summary: This paper proposes a decomposition based multiobjective memetic algorithm for multiresolution community detection in complex networks. The method models the problem as a multiobjective optimization problem and combines evolutionary algorithm with local search to detect communities at multiple resolution levels. Experimental results demonstrate the effectiveness of the method.
Article
Computer Science, Artificial Intelligence
Yongfeng Li, Lingjie Li, Qiuzhen Lin, Ka-Chun Wong, Zhong Ming, Carlos A. Coello
Summary: This paper proposes a self-organizing weighted optimization based framework (S-WOF) for solving large-scale multi-objective optimization problems (LSMOPs). The S-WOF achieves a dynamic trade-off between convergence and diversity by adjusting the weights and evaluation numbers. In addition, an efficient competitive swarm optimizer (CSO) is implemented to improve the search ability. Experimental results demonstrate the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Lingjie Li, Yongfeng Li, Qiuzhen Lin, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a convergence and diversity guided leader selection strategy (CDLS) for improving the performance of particle swarm optimizer (PSO) in high-dimensional objective space. By adaptively selecting different leader particles based on each particle's situation, CDLS achieves a good tradeoff between convergence and diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Junchuang Cai, Qingling Zhu, Qiuzhen Lin
Summary: This paper introduces a new Dynamic Pickup and Delivery Problem (DPDP) and proposes an algorithm called VNSME to solve it. The algorithm performs well in practical scenarios and achieves the first place in the ICAPS 2021 competition.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Xunfeng Wu, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan, Victor C. M. Leung
Summary: In this article, an ensemble surrogate-based coevolutionary optimizer is proposed to solve large-scale optimization problems. By training local surrogate models and using feature selection to construct a selective ensemble surrogate, the optimizer approximates the target problem. With two populations solving the target problem and a simplified auxiliary problem collaboratively, the coevolutionary optimizer can leverage the search experience from the auxiliary problem to help solve the target problem.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Junkai Ji, Jiajun Zhao, Qiuzhen Lin, Kay Chen Tan
Summary: The dendritic neural model (DNM) is a computationally fast machine-learning technique that can be implemented using logic circuits and binary calculations. In order to enhance its speed, a more concise architecture can be generated. However, existing multiobjective evolutionary algorithms face limitations in solving this large-scale multiobjective optimization problem. Therefore, a novel competitive decomposition-based algorithm is proposed in this study, which outperforms state-of-the-art algorithms in terms of optimization ability. Experimental results also demonstrate that the proposed algorithm can achieve competitive performance when applied to DNM and its hardware implementation, compared to widely used machine-learning approaches.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yuchao Su, Qiuzhen Lin, Zhong Ming, Kay Chen Tan
Summary: This article proposes an effective method called Adapted Decomposed Directions (ADDs) for solving Multiobjective Optimization Problems (MOPs). Instead of using a single ideal or nadir point, each weight vector has its own ideal point for decomposition, and the decomposed directions are adaptively adjusted during the search process. The experimental results show that this method significantly improves the performance of three representative Multiobjective Evolutionary Algorithms (MOEAs) and outperforms seven competitive MOEAs in solving various artificial MOPs and a real-world MOP.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lijia Ma, Zengyang Shao, Xiaocong Li, Qiuzhen Lin, Jianqiang Li, Victor C. M. Leung, Asoke K. Nandi
Summary: This article proposes an evolutionary deep reinforcement learning algorithm called EDRL-IM for influence maximization in complex networks. By combining evolutionary algorithm and deep reinforcement learning algorithm, EDRL-IM outperforms state-of-the-art methods in finding seed nodes.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Zhaobo Liu, Guo Li, Haili Zhang, Zhengping Liang, Zexuan Zhu
Summary: In this article, we propose a new multifactorial evolutionary algorithm based on diffusion gradient descent (MFEA-DGD) for multitasking optimization. The MFEA-DGD implements knowledge transfer among optimization tasks and ensures convergence through complementary crossover and mutation operators. Experimental results show that MFEA-DGD outperforms state-of-the-art EMT algorithms in terms of convergence speed and competitive results, and the convexity of different tasks provides interpretability of the experimental results.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
