Article
Computer Science, Artificial Intelligence
Tianri Wang, Pengzhi Zhang, Juan Liu, Minmin Zhang
Summary: This paper investigates the Cloud Manufacturing Service Selection and Scheduling (CMSSS) problem, constructs an eight-objective optimization model, and designs a many-objective evolutionary algorithm MaOEA-AES to address the issue. By using diversity-based population partition technology and adaptive penalty boundary intersection distance, the algorithm maintains population diversity and selects elitist solutions effectively.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Industrial
Fei Wang, Yuanjun Laili, Lin Zhang
Summary: Service composition is a core issue in cloud manufacturing, with this paper focusing on two types of correlations and proposing a mathematical model and a many-objective genetic algorithm based on correlation. Experiments show the effectiveness of the algorithm in eliminating infeasible search space and providing high QoS service composition solutions.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Information Systems
Zhixia Zhang, Mengkai Zhao, Hui Wang, Zhihua Cui, Wensheng Zhang
Summary: This paper explores task scheduling in cloud computing and presents an interval many-objective optimization model and evolutionary algorithm, which consider uncertain factors while improving scheduling efficiency and performance.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Maoqing Zhang, Lei Wang, Weian Guo, Wuzhao Li, Dongyang Li, Bo Hu, Qidi Wu
Summary: This paper proposes a relative non-dominance matrix and fitness formula to address the issue of dominance resistance in multi-objective optimization. Empirical analyses show that solutions with smaller fitness values are more likely to dominate other solutions in the evolutionary process and play a critical role in converging towards the true Pareto fronts. Additionally, the combination of k-means clustering strategy and the relative non-dominance matrix ensures diversity and adaptively adjusts the parameter k for environmental selection design.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Zhixia Zhang, Hui Wang, Wensheng Zhang, Zhihua Cui
Summary: A cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithm (MaOEA-GM) is proposed to address the conflicts between convergence and diversity and the lack of Pareto selection pressure in many-objective optimization problems (MaOPs). The algorithm includes a competition stage with a strategy pool and a new game utility function to balance convergence and diversity, and a cooperative stage where individuals choose their preferred environmental selection mechanism through voting. Experimental results show that the MaOEA-GM algorithm outperforms five advanced MaOEAs in terms of convergence, diversity, and competitiveness in solving MaOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yankai Wang, Shilong Wang, Song Gao, Xixuan Guo, Bo Yang
Summary: This paper proposes a dynamic service composition reconfiguration model to address the service reconfiguration issue in real-life cloud manufacturing environments, redefining three objectives and utilizing an adaptive multi-population multi-objective whale optimization algorithm for optimization. The results show that the proposed model can effectively tackle the issue and outperforms other algorithms, enhancing the robustness of service composition reconfiguration in cloud manufacturing.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Sanyan Chen, Xuewu Wang, Jin Gao, Wei Du, Xingsheng Gu
Summary: The paper introduces an adaptive switching strategy-based evolutionary algorithm to address the selection pressure and diversity issues in many-objective optimization. The algorithm dynamically switches between two deletion criteria in each generation to effectively remove poor solutions, demonstrating its effectiveness and advantages through comparisons with state-of-the-art algorithms on benchmark problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yifan Gao, Bo Yang, Shilong Wang, Zhengping Zhang, Xiaoli Tang
Summary: This paper studies a bi-objective service composition and optimal selection problem considering QoS and robustness and develops a strengthened multi-objective gray wolf optimizer to solve it. By introducing improvement strategies, the proposed algorithm outperforms its competitors in terms of convergence and population diversity.
APPLIED SOFT COMPUTING
(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
Jiawei Yuan, Hai-Lin Liu, Fangqing Gu, Qingfu Zhang, Zhaoshui He
Summary: This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance, and proposes an algorithm for solution evaluation using a ratio-based indicator. By identifying promising regions and ensuring population diversity, the algorithm demonstrates competitive performance on various benchmark problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Songbai Liu, Junhao Zheng, Qiuzhen Lin, Kay Chen Tan
Summary: This paper proposes an MOEA using clustering with a flexible similarity metric to tackle multi and many-objective optimization problems with irregular Pareto fronts. By predicting the concavity or convexity of the problem and setting a flexible reference point, the algorithm can properly measure the similarity between solutions and classify them effectively in environmental selection, showing significant advantages in experimental results.
INFORMATION SCIENCES
(2021)
Article
Mathematics
Chengxin Wen, Hongbin Ma
Summary: Many-objective optimization is an important research topic in evolutionary computing, and a two-stage hypervolume-based evolutionary algorithm is proposed to achieve convergence and diversity through global and local searches. Experimental results show that the algorithm is competitive in most cases.
Article
Mathematics
Yizhang Xia, Jianzun Huang, Xijun Li, Yuan Liu, Jinhua Zheng, Juan Zou
Summary: This paper discusses the balance between convergence and diversity in many-objective evolutionary optimization algorithms. The authors propose a new algorithm called Indicator and Decomposition-based Evolutionary Algorithm (IDEA) to achieve both convergence and diversity. Experimental results show that IDEA outperforms other state-of-the-art many-objective algorithms.
Article
Engineering, Chemical
Wendi Xu, Xianpeng Wang, Qingxin Guo, Xiangman Song, Ren Zhao, Guodong Zhao, Yang Yang, Te Xu, Dakuo He
Summary: Single-objective to multi-objective/many-objective optimization is a new paradigm in evolutionary transfer optimization. Theoretical insights into this area are relatively rare, so we propose a study on the theoretical advances of multi-objective optimization based on a case study of a permutation flow shop scheduling problem.
Article
Operations Research & Management Science
Jianghua Zhang, Felix T. S. Chan, Xinsheng Xu
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Industrial
Jianghua Zhang, Felix T. S. Chan, Xinsheng Xu
Summary: This paper examines the optimal purchasing decisions of a buyer in combined procurement, considering the liquidity of spot trading. The results are verified through numerical analysis and sensitivity analysis, and management insights are provided.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Transportation
Kam K. H. Ng, Felix T. S. Chan, Yichen Qin, Gangyan Xu
Summary: The minimization of approaching time and the maximization of the utilization of air route and airport resources are the two main goals of air traffic control. This research investigates the impact of air traffic controller specific parameters on the arrival sequence and uncertain arrival times at entry waypoints of flights. The proposed two-stage optimization framework determines a deterministic schedule and optimizes the approaching time while considering additional factors such as longitudinal separation and cruise speed adjustment.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Engineering, Industrial
Abdelrahman E. E. Eltoukhy, Mohamed Hussein, Min Xu, Felix T. S. Chan
Summary: This study proposes a vehicle routing problem (VRP) model suitable for modular integrated construction (MiC) and investigates the interdependence between the resource allocation problem (RAP) and the proposed VRP model. A blockchain system is also developed to secure information sharing. The study develops a coordinated system model using a nested ant colony optimization-based algorithm and validates its effectiveness through a real case study. The results demonstrate that the model provides applicable plans for both construction sites and logistics companies.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(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, Information Systems
Xinxin Li, Shuai Hua, Qunfeng Liu, Yun Li
Summary: This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve the issue of global convergence. The framework alternates between regular partitions and evolutions of populations, ensuring global convergence. The framework is applied to particle swarm optimization, differential evolution, and genetic algorithm, resulting in improved global convergence and performance compared to the original versions.
INFORMATION SCIENCES
(2023)
Article
Engineering, Civil
Qing Zhang, Felix T. S. Chan, Xiaowen Fu
Summary: This study focuses on the operational aircraft maintenance routing problem with cruise speed control. The goal is to minimize the quantity of required aircraft by optimizing cruise times and determining aircraft routes. The study proposes a preprocessing step to reduce the network size and an improved ant colony optimization algorithm with new mechanisms for cruise time optimization and search efficiency enhancement.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
Article
Green & Sustainable Science & Technology
Kelvin K. Orisaremi, Felix T. S. Chan, Xiaowen Fu, Nick S. H. Chung
Summary: A major goal of the SE4ALL Initiative is to improve energy efficiency through an energy mix. This study proposes a methodology for estimating the required power from flare gas using directional distance DEA model. The results show that an optimal energy mix for Venezuela includes 40% gas-based power and 60% renewable energy, which can meet the nation's energy shortage more than three times over.
JOURNAL OF CLEANER PRODUCTION
(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
Green & Sustainable Science & Technology
Muhammad Saleem Sumbal, Waqas Ahmed, Huzeifa Shahzeb, Felix Chan
Summary: This study investigates the challenges faced by the transportation industry in Pakistan during and after COVID-19 and proposes sustainable strategies to combat these challenges. The findings reveal challenges such as reduced import-export, limited supplies, increased e-commerce, and operational issues. Strategies such as contactless deliveries, e-commerce expansion, tech-based performance management, and digital trucking are recommended for sustainable development in the transportation and logistics sector.
Article
Business
Rajeev Ranjan Kumar, Felix T. S. Chan, Abhishek Chakraborty, Mohit Goswami
Summary: This study investigates the competition and relative positioning among vehicle manufacturers in a supply chain under government policy interventions. By developing a game-theoretic model, we examine three different supply chain structures and generate multifaceted insights for the government, manufacturers, and consumers. The results show that higher social welfare can be generated when either tax or subsidy is deployed in the case of nondominance.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Gongli Luo, Xiaoqing Liu, Felix T. S. Chan
Summary: This paper examines the optimal ordering decisions of a newsvendor in portfolio procurement involving long-term contracts and spot purchases. The study indicates that the newsvendor's optimal ordering quantity changes with market parameters and is significantly influenced by spot price fluctuation in portfolio procurement. The research proposes a method of using relative fluctuation of spot price and long-term contract price, which is more applicable in practice. Numerical experiments verify the results and provide management insights.
Article
Environmental Sciences
Dheeraj Narang, Jitender Madaan, Felix T. S. Chan, Ekachidd Chungcharoen
Summary: Water is a limited and invaluable resource for human survival, but negligence and unregulated water use have caused a global water crisis. The Internet of Things (IoT) can be used to integrate decision-making and information to drive the development of the water value chain. By analyzing the factors for implementing IoT in an open-loop water value chain, this study identifies the ecosystem of the IoT network, network configuration and adaptation, and data mobility as the most important enablers.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2024)
Article
Mathematics
Kelvin K. Orisaremi, Felix T. S. Chan, Xiaowen Fu
Summary: Economic growth is crucial for nations with natural resources. This study proposes an inverse data envelopment analysis model to assess the optimal increase in input resources required for economic growth among OPEC member nations, and identifies the economic growth potential for each member country.
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)
