Article
Computer Science, Artificial Intelligence
Mariusz Oszust
Summary: The paper presents an improved MPA variant using a Local Escaping Operator (LEO) to address the premature convergence issue. Experimental results demonstrate the superiority of LEO-MPA over MPA and recent algorithms, showing the effectiveness of hybridizing meta-heuristics with LEO for optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Doaa Abdulmoniem Abdulgader, Adil Yousif, Awad Ali
Summary: This paper presents a new task-scheduling mechanism based on the Discrete Prey-Predator algorithm to optimize the task-scheduling process in cloud computing. The mechanism assigns survival values to scheduling solutions, aiming to minimize task execution time and improve overall system efficiency and resource utilization. Simulation results show that the proposed mechanism has shorter execution times compared to the firefly algorithm.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics
Khudhayr A. Rashedi, Mohd Tahir Ismail, Nawaf N. Hamadneh, S. Al Wadi, Jamil J. Jaber, Muhammad Tahir
Summary: The use of artificial intelligence technology in business process optimization has a significant impact on a country's economic development. This paper proposed a mathematical model using artificial neural networks to detect outliers in the daily returns of the Saudi stock market, demonstrating the potential of AI optimization techniques in solving business process problems.
JOURNAL OF MATHEMATICS
(2021)
Article
Agriculture, Dairy & Animal Science
Sergio Fernandez Moya, Carlos Iglesias Pastrana, Carmen Marin Navas, Maria Josefa Ruiz Aguilera, Juan Vicente Delgado Bermejo, Francisco Javier Navas Gonzalez
Summary: This study examines the predator/prey interaction and identifies factors such as predator species, age, status, time elapsed, prey species, and terrain relief that affect the success of prey escaping from big cats. The findings provide insights into the predatory abilities and anti-predation strategies of big cats, and can be used to improve environmental enrichment programs in captivity and design selective strategies for domestic animals.
Article
Physics, Multidisciplinary
Salih Djilali, Soufiane Bentout, Behzad Ghanbari, Sunil Kumar
Summary: This paper presents various systems that investigate the interaction between two populations, such as predator-prey interaction, mutualism interaction, and competitive interaction. The models consider the contradictory behaviors of the first population - herd behavior for some and solitary demeanor for others - and study the effects of these behaviors on the interaction between the two populations.
Article
Mathematics, Applied
Sainan Wu
Summary: This paper considers a reaction-diffusion predator-prey model with indirect prey-taxis and predator-taxis. The model obtains globally bounded solutions under different parameters and conditions.
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
(2022)
Article
Mathematics, Applied
Guoqiang Ren, Bin Liu
Summary: In this work, we considered a two-species predator-prey chemotaxis model and proved that it admits a global boundeness of classical solutions in any physically meaningful dimension. By carefully balancing the triple cross diffusion, we showed that the global classical solutions exponentially converge to a constant stable steady state.
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS
(2022)
Article
Mathematics, Applied
Liangying Miao, He Yang, Shengmao Fu
Summary: This paper investigates the global boundedness of a two-species predator-prey chemotaxis model with one signal. By establishing the uniform boundedness of the L-2-norm for prey and predator densities and the uniform boundedness of the L-4-norm for the signal gradient, the study considers the global existence and boundedness of classical solutions to the model in a three-dimensional bounded domain.
APPLIED MATHEMATICS LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Fazhan Zhang, Yichao He, Haibin Ouyang, Wenben Li
Summary: An enhanced group theory-based optimization algorithm (EGTOA) is proposed to solve the uncapacitated facility location problem (UFLP) quickly and effectively. By introducing a new local search operator and a redundant checking strategy, EGTOA outperforms existing algorithms in terms of solution quality and speed.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Applied
Guoqiang Ren, Yu Shi
Summary: This paper investigates the global boundedness and stability of solutions for a prey-taxis model with handling and searching predators in a two-dimensional bounded domain with smooth boundary. By deriving entropy-like equations and boundedness criteria, the paper proves the existence of a unique uniformly bounded global classical solution, and demonstrates that the prey-only steady state is globally asymptotically stable when the predator is weak. The paper also derives the convergence rate of solutions to the steady states.
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Rohit Salgotra, Supreet Singh, Urvinder Singh, Seyedali Mirjalili, Amir H. Gandomi
Summary: This paper proposes a hybrid algorithm MpNMRA, which combines the strengths of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to solve high-dimensional problems. Experimental results show that the proposed algorithm performs excellently in benchmark tests, as well as in solving real-world optimization problems and training multi-layer perceptrons.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Varun Ojha, Jon Timmis, Giuseppe Nicosia
Summary: This study conducts a comprehensive sensitivity analysis on the hyperparameters of different optimization algorithms, revealing their influence patterns and interaction effects on algorithm performance, providing guidance for algorithm configuration.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Industrial
Grzegorz Filcek, Jerzy Jozefczyk, Miroslaw Lawrynowicz
Summary: This study considers a joint location and scheduling problem involving selecting a number of executor locations and task plans. A heuristic algorithm Alg BC is proposed, utilizing the NSGA II scheme for multi-objective optimization. The performance of Alg BC is evaluated for small instances, and sensitivity analysis is provided for larger instances.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2021)
Article
Engineering, Civil
Chao Wang, Rongrong Liu, Yansen Su, Xingyi Zhang
Summary: The electric location-routing problem involves optimizing electric vehicle routing and charging facility location. Existing algorithms often use a two-phase search strategy to optimize routing and location alternately, but they are criticized for inefficiency as problem size increases. To improve search efficiency, we propose an accelerating two-phase multiobjective evolutionary algorithm that uses a learning method to extract useful information from historical searches and generate high-quality routing and location solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Mathematics, Interdisciplinary Applications
Lei Kong, Fengjiao Lu
Summary: The influence of indirect prey-taxis on the dynamics of a predator-prey system with predator functional response is studied. The study analyzes the stability and bifurcations of the system, deriving critical values of the indirect prey-taxis coefficient. The research finds that attractive indirect prey-taxis can destabilize the system and induce the emergence of spatially inhomogeneous periodic solutions. The secretion level of chemoattractant by the prey plays a role in determining the likelihood of spatial patterns.
CHAOS SOLITONS & FRACTALS
(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)