Article
Automation & Control Systems
Zi-Jia Wang, Yu-Ren Zhou, Jun Zhang
Summary: This article presents a parameter-free niching method based on adaptive estimation distribution (AED) and develops a distributed differential evolution (DDE) algorithm, called AED-DDE, for solving multimodal optimization problems (MMOPs). The algorithm improves population diversity through a multiniche co-evolution mechanism and refines solution accuracy through probabilistic local search.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Aleksander Skakovski, Piotr Jedrzejowicz
Summary: The paper introduces a novel multisize island model and an improved algorithm with automatic optimization of the number of islands, showing higher performance and efficiency, especially in concurrent execution on multiple computational units.
Article
Computer Science, Hardware & Architecture
Man Zhao, Guoyang Li, Hui Li, Shenglong Li
Summary: The continuing growth of space debris poses a significant threat to on-orbit operations. To address this issue, this article proposes a novel adaptive multipopulation differential evolutionary algorithm based on a theoretical model specialized in the scheduling of space debris monitoring resources. Experimental results demonstrate the effectiveness and reliability of the proposed algorithm in ensuring the safety of on-orbit operations.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Computer Science, Information Systems
Kun Li, Huixin Tian
Summary: This paper introduces a bagging based multiobjective differential evolution algorithm with multiple subpopulations, which competes and cooperates to generate offspring solutions, effectively maintaining search diversity. Experimental results on 22 benchmark problems show that the proposed algorithm outperforms several state-of-the-art MODEs and other multiobjective evolutionary algorithms in the literature.
Article
Automation & Control Systems
Jun-Xian Chen, Yue-Jiao Gong, Wei-Neng Chen, Mengting Li, Jun Zhang
Summary: This article introduces a novel elastic differential evolution algorithm for automatic data clustering, which adapts the number of clusters and centroids through variable-length encoding and evolution operators, eliminating encoding redundancy. The algorithm considers each clustering layout as a whole and includes subspace crossover and two-phase mutation operators to properly exchange information among individuals of different lengths.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Chemistry, Multidisciplinary
Angela Hsiang-Ling Chen, Yun-Chia Liang, Jose David Padilla
Summary: This paper investigates the characteristics of MRCPSP/max under uncertainty and emphasizes the importance of managerial ability to recognize and handle disruptions effectively. Using the entropy approach, disruptive events and response time intervals are identified. The problem is solved using a resilient three-stage procedure that measures schedule robustness and adaptivity. The differential evolution algorithm (DDE) is proposed and evaluated against the best known optima, showing its effectiveness in managing disruptions. The stability of solutions provided by DDE is particularly robust when response times are added within a certain range.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Ismail M. Ali, Daryl Essam, Kathryn Kasmarik
Summary: A novel technique is proposed in this paper to make a simple differential evolution algorithm effective for solving binary-based problems, introducing new components and fitness evaluation approach. Experimental results demonstrate the superiority of this new algorithm in terms of solution quality and computational times compared to other state-of-the-art algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Xiaosi Li, Kaiyu Wang, Haichuan Yang, Sichen Tao, Shuai Feng, Shangce Gao
Summary: Differential evolution algorithm shows good performance but suffers from local optimal trapping and premature evolution issues. PAIDDE algorithm improves DE by utilizing information feedback, outperforming other state-of-the-art algorithms in solution quality across various benchmark functions and real-world problems.
Article
Plant Sciences
Helen M. Cockerton, Shiv S. Kaundun, Lieselot Nguyen, Sarah Jane Hutchings, Richard P. Dale, Anushka Howell, Paul Neve
Summary: The study reveals a positive correlation between glyphosate resistance level and EPSPS gene copy number, identifying gene amplification as the resistance mechanism. Glyphosate-resistant A. tuberculatus plants exhibit lower competitive responses and a growth trade-off associated with gene amplification mechanism under intra-phenotypic competition.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Automation & Control Systems
Sheng-Hao Wu, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: This article proposes a new evolutionary multitask optimization algorithm (EMTO) to address the similarity measurement and knowledge transfer issues. By considering the shift invariance between tasks, the proposed algorithm clusters similar tasks and transfers successful parameters among them. Experimental results demonstrate the superiority of the proposed algorithm in solving many-task optimization problems (MaTOPs).
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: Feature selection aims to reduce data dimensionality and classification error rate through a multiobjective approach. This article proposes a niching-based method that minimizes the number of selected features and the classification error rate simultaneously. The proposed method can generate a diverse set of feature subsets with good convergence and distribution, and with almost the same lowest classification error rate for the same number of features.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Shangce Gao, Yang Yu, Yirui Wang, Jiahai Wang, Jiujun Cheng, MengChu Zhou
Summary: The article introduces a novel variant of the JADE algorithm that improves its performance by incorporating chaotic local search mechanisms. Experimental and statistical analyses demonstrate the superior performance of this variant compared to traditional JADE and other state-of-the-art optimization algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wei Li, Xiang Meng, Ying Huang
Summary: The theory of fitness landscape is applied to explain the behavior of evolutionary algorithms in solving optimization problems in biological evolution. By analyzing the features of fitness landscape, it can help to understand the difficulty of solving optimization problems and the distribution of optimal solutions.
Article
Automation & Control Systems
Kai Wang, Wenyin Gong, Zuowen Liao, Ling Wang
Summary: This article proposes a hybrid niching-based differential evolution algorithm with two archives, HNDE/2A, for locating multiple roots of nonlinear equation systems within a limited computational budget. By combining the techniques of crowding and speciation, as well as utilizing a root archive and an inferior offspring archive, the algorithm achieves better results in terms of root ratio and success rate compared to other algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jing Liang, Xuanxuan Ban, Kunjie Yu, Boyang Qu, Kangjia Qiao
Summary: In this paper, a rankings-based fitness function method is designed for efficiently selecting and utilizing promising infeasible solutions in solving constrained optimization problems using evolutionary algorithms. The method dynamically adjusts weights to balance constraints and objectives, and generates promising offspring using three differential evolution strategies. Experimental results show the proposed method's superior performance compared to other state-of-the-art methods, especially in solving real-world problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mingshan You, Jiao Yin, Hua Wang, Jinli Cao, Kate Wang, Yuan Miao, Elisa Bertino
Summary: This paper proposes an algorithm for constructing an access control knowledge graph based on user and resource attributes, and introduces an online learning framework. The experimental results demonstrate that topological features extracted from the knowledge graph can enhance access control performance in scenarios with varying degrees of class imbalance.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Engineering, Biomedical
Supriya Supriya, Siuly Siuly, Hua Wang, Yanchun Zhang
Summary: Epilepsy, a chronic brain disorder, poses challenges in diagnosis and treatment. Graph-theory based automated epilepsy detection methods have emerged as a promising approach to analyze the complex nature of EEG signals and understand brain activity. This paper provides a comprehensive review of such methods, aiming to assist neurologists and researchers in improving epilepsy diagnosis and developing intelligent systems.
IEEE REVIEWS IN BIOMEDICAL ENGINEERING
(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
Feng-Feng Wei, Wei-Neng Chen, Qing Li, Sang-Woon Jeon, Jun Zhang
Summary: This article defines distributed expensive constrained optimization problems (DECOPs) and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE adaptively evolves different constraints in an asynchronous way through on-demand evaluation, improving population convergence and diversity. Experimental results demonstrate that DEAOE outperforms centralized state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) in terms of performance and efficiency.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Civil
Wanyi Zhou, Xiaolin Xiao, Yue-Jiao Gong, Jia Chen, Jun Fang, Naiqiang Tan, Nan Ma, Qun Li, Chai Hua, Sang-Woon Jeon, Jun Zhang
Summary: This study proposes a method of formulating the traffic network as a temporal attributed graph and performing node representation learning on it to address the problem of travel time estimation. The learned representation can jointly exploit dynamic traffic conditions and the topology of the road network, and estimate travel time using a route-based spatio-temporal dependence learning module.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Yue-Jiao Gong, Ting Huang, Yi-Ning Ma, Sang-Woon Jeon, Jun Zhang
Summary: This paper focuses on the multiple-trajectory planning problem for automatic underwater vehicles (AUVs) and proposes a comprehensive model that considers the complexity of underwater environments, efficiency of each trajectory, and diversity among different trajectories. To solve this problem, an ant colony-based trajectory optimizer is developed, incorporating a niching strategy, decayed alarm pheromone measure, and diversified heuristic measure for improved search effectiveness and efficiency. Experimental results demonstrate that the proposed algorithm not only provides multiple AUV trajectories for flexible choice, but also outperforms state-of-the-art algorithms in terms of single trajectory efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junna Zhang, Degang Chen, Qiang Yang, Yiqiao Wang, Dong Liu, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel differential evolution framework called proximity ranking-based multimodal differential evolution (PRMDE) for multimodal optimization. Through the cooperative cooperation among three main mechanisms, PRMDE is capable of locating multiple global optima simultaneously. Experimental results show that PRMDE is effective and achieves competitive or even better optimization performance than several representative methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liu, Hao He, Qiang Yang, Yiqiao Wang, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Ashik Mostafa Alvi, Siuly Siuly, Hua Wang
Summary: Mild cognitive impairment (MCI) is an irreversible degenerative disorder that may lead to dementia in elderly people. Early identification is crucial for effective treatment. This research proposes a deep learning-based framework using EEG data to identify MCI individuals from healthy volunteers, achieving high accuracy and sensitivity. The proposed model provides a robust biomarker and can guide the development of an automatic diagnosis system for MCI detection.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Jia Wang, Jun-Rong Jian, Zhi-Hui Zhan, Yun Li, Sam Kwong, Jun Zhang
Summary: This article proposes a method called GT-based DE to solve large-scale optimization problems by targeting and modifying certain values in bottleneck dimensions. Experimental results show that GTDE is efficient and performs better or at least comparable to other state-of-the-art algorithms in solving LSOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Min Gao, Jian-Yu Li, Chun-Hua Chen, Yun Li, Jun Zhang, Zhi-Hui Zhan
Summary: In this study, an enhanced MKR (EMKR) approach is proposed to address the two difficult issues in knowledge graph-based recommender systems. The attention mechanism and relation-aware graph convolutional neural network are utilized to capture users' historical behavior patterns and deep multi-relation semantic information. Additionally, a two-part modeling strategy is introduced for better representation of users in datasets with different sparsity. Experimental results show that EMKR outperforms state-of-the-art approaches, especially in sparse user-item interactions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Automation & Control Systems
Feng-Feng Wei, Wei-Neng Chen, Wentao Mao, Xiao-Min Hu, Jun Zhang
Summary: This article proposes an efficient two-stage surrogate-assisted differential evolution (eToSA-DE) algorithm to handle expensive inequality constraints. The algorithm trains a surrogate model for the degree of constraint violation, with the type of surrogate changing during the evolution process. Both types of surrogates are constructed using individuals selected by the boundary training data selection strategy. A feasible exploration strategy is devised to search for promising areas. Extensive experiments demonstrate that the proposed method can achieve satisfactory optimization results and significantly improve the efficiency of the algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao-Fang Liu, Jun Zhang, Jun Wang
Summary: This article presents a cooperative differential evolution algorithm with an attention-based prediction strategy for dynamic multiobjective optimization. Multiple populations are used to optimize multiple objectives and find subparts of the Pareto front. The algorithm achieves a balanced approximation of the Pareto front and adapts to changes in the environment by using a new attention-based prediction strategy. Experimental results demonstrate the superiority of the proposed method to state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ye-Qun Wang, Jian-Yu Li, Chun-Hua Chen, Jun Zhang, Zhi-Hui Zhan
Summary: This research proposes a particle swarm optimization approach called SAFE-PSO that tackles the optimization problem of neural networks. Experimental results show that SAFE-PSO is effective and efficient on widely used datasets.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
