Article
Engineering, Environmental
K. G. Aparna, R. Swarnalatha
Summary: This research proposes a novel dynamic optimization control system using a non-dominated sorting-based multi-objective cuckoo search optimization algorithm (NSMOCS). By evaluating the WWTP model, a Pareto front was obtained. Contribution analysis was used to find the weight values of the objectives and the best tuning parameters for the PI controller were found using the optimization algorithm. The simulation results showed reductions in pollution units per day and operating cost index under different weather conditions.
JOURNAL OF WATER PROCESS ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
S. Mohan, Akash Sinha
Summary: This paper proposes a novel method for performing nondominated sorting in a multiobjective optimization problem using a modified directional Bat algorithm. Unlike NSGA-II, the proposed algorithm generates and compares new solutions with all previous solutions, reducing computational time and generating diverse solutions. A unique sorting method using a Nondomination matrix is introduced, which can be easily updated to include new solutions and preserve elitism. Detailed criteria are provided for the selection of new solutions. Experimental results show that the proposed algorithm is competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most benchmark optimization problems. The algorithm also provides a standardized platform for nondomination sorting, applicable to any other metaheuristic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Long, Guoquan Li, Lin Jiang
Summary: Non-dominated sorting is crucial for MOEAs, with the introduced DNS method showing superior performance compared to other methods. The computational complexity of DNS is O(mN log N), and its effectiveness is validated through numerical experiments. The DNSGA, based on the DNS method, outperforms other MOEAs on various multi-objective problems.
Article
Biodiversity Conservation
Hongjiang Liu, Fengying Yan, Hua Tian
Summary: The rational allocation of land use is crucial in the development of low-carbon cities. Existing models for optimizing land use allocation often lack comprehensive consideration of quantitative and spatial objectives, as well as efficient algorithms. This paper proposes a patch-based optimization model that addresses these gaps and provides support for low-carbon land use planning.
ECOLOGICAL INDICATORS
(2022)
Article
Energy & Fuels
Zheng Jing, Chunhua Zhang, Panpan Cai, Yangyang Li, Zhaoyang Chen, Songfeng Li, An Lu
Summary: The study found that BMEP and CCR have a trade-off impact on BTE and BS emissions, with BMEP increase leading to higher BTE but also increased BSNOx, while higher CCR resulted in low BSNOx but deteriorated BTE, BSCO, and BSHC.
Article
Materials Science, Paper & Wood
Baogang Wang, Chunmei Yang, Yucheng Ding
Summary: This study proposed an improved multi-objective algorithm for solid wood panel layout optimization, addressing the issues of weak convergence ability, single-objective optimization, and poor optimization effect of traditional genetic algorithms. By increasing the search capability, improving the optimization effect, and achieving simultaneous optimization of multiple objectives, the improved algorithm showed better optimization and stability compared to the traditional algorithm.
Article
Green & Sustainable Science & Technology
Chen Zhang, Tao Yang
Summary: The frequent failures and high maintenance costs of wind turbines in wind farms have a significant impact on the stable development of wind power. This study establishes an optimal model for maintenance planning and resource allocation in wind farms under various constraints, aiming to address the dynamic maintenance needs efficiently.
Article
Green & Sustainable Science & Technology
Senthilkumar Subramanian, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju, Lucian Mihet-Popa
Summary: A new Multi-Objective Optimization based genetic algorithm model was proposed for wind energy system, which showed superior performance in maximizing power output and minimizing energy cost. The algorithm was analyzed in MATLAB/Simulink platform, demonstrating higher turbine power output.
Article
Automation & Control Systems
Mansoureh Hasannia Kolaee, Seyed Mohammad Javad Mirzapour Al-e-Hashem, Armin Jabbarzadeh
Summary: Currently, the medical tourism industry is growing rapidly and aims to provide affordable and high-quality medical services to patients worldwide. This paper addresses the problem of designing medical tourism trips, where patients travel from their home city to a destination city in another country in order to receive medical care. The proposed multi-objective optimization model considers both the cost and the attractiveness of trips, and a local search-based genetic algorithm is proposed to solve the problem. The findings suggest a trade-off between cost and attractiveness in decision-making, while also providing high-quality solutions in a reasonable amount of time.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Wu Deng, Xiaoxiao Zhang, Yongquan Zhou, Yi Liu, Xiangbing Zhou, Huiling Chen, Huimin Zhao
Summary: This paper proposes an enhanced fast NSGA-II algorithm (ASDNSGA-II) for solving multi-modal multi-objective optimization problems. By using a special congestion strategy and adaptive crossover strategy, ASDNSGA-II improves the distribution and convergence of solutions. Experimental results show that ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of solutions.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Lu Sun, Bao Zhang, Ping Wang, Zhihong Gan, Pengpeng Han, Yijian Wang
Summary: The process of intelligent multi-objective parametric optimization design for mirrors is discussed, with the error of the mirror surface shape and the total mass being examined as the optimization objectives. The establishment of complex objective functions for solving the optimization problem of the mirror surface shape error was realized, and manual modification of the model was avoided. Moreover, combining this with a non-dominated sorting genetic algorithm (NSGA) helped the Pareto front move towards an ideal optimal set of solutions.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Physical
Milan Joshi, Ranjan Kumar Ghadai, S. Madhu, Kanak Kalita, Xiao-Zhi Gao
Summary: The increasing popularity of micro-machining has led to widespread use of micro-turning and micro-milling in the manufacturing industry, where optimizing cutting parameters is crucial for performance but often presents conflicting objectives. This study utilizes metaheuristic multi-objective optimization algorithms to address this issue and compares the performance of NSGA-II, MOALO, and MODA in micro-machining applications by using the COPRAS method for assessing Pareto solutions.
Article
Engineering, Aerospace
Abu Bakar, Ke Li, Haobo Liu, Ziqi Xu, Marco Alessandrini, Dongsheng Wen
Summary: This paper presents a framework for the design of low Reynolds number airfoil, using a convolutional neural network for aerodynamic coefficient prediction and a non-dominated sorting genetic algorithm for multi-objective optimization. The results show that the proposed CNN can accurately predict the aerodynamic coefficients and obtain actual optimization results in less time.
Article
Engineering, Multidisciplinary
Sushmita Sharma, Nima Khodadadi, Apu Kumar Saha, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: This paper presents a method to solve multi-objective optimization problems using the Butterfly Optimization Algorithm (BOA). The BOA is improved and extended to tackle multi-objective problems. Experimental results show that the new MONSBOA algorithm outperforms other algorithms in solving various types of problems.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Pradeep Jangir, Ghanshyam G. Tejani, Manoharan Premkumar
Summary: The article discusses how to improve the Heat Transfer Search algorithm for solving multi-objective problems using decomposition, and conducts empirical research on real structural problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
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)