Article
Computer Science, Artificial Intelligence
Junbo Lian, Guohua Hui
Summary: This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), which is a metaheuristic algorithm inspired by human evolution. The algorithm divides the global search process into two distinct phases and uses unique search strategies. Comparative analysis with other algorithms demonstrates the effectiveness of HEOA in approximating optimal solutions for complex global optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Interdisciplinary Applications
Simrandeep Singh, Nitin Mittal, Diksha Thakur, Harbinder Singh, Diego Oliva, Anton Demin
Summary: Image processing is a significant area of growth in the current scenario, with segmentation being a key step, where multilevel thresholding methods play an important role, and various optimization techniques can enhance the performance of image processing.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Review
Physics, Multidisciplinary
Zhenwu Wang, Chao Qin, Benting Wan, William Wei Song
Summary: This study conducted a comprehensive survey and comparison of nature-inspired optimization algorithms, evaluating their accuracy, stability, efficiency, and parameter sensitivity, and providing a systematic summary of challenging problems and research directions.
Review
Computer Science, Interdisciplinary Applications
Kutub Thakur, Gulshan Kumar
Summary: Network services' operational and reliable operation has become a necessity in today's society, with the growing threat of intruders. Nature-inspired techniques show great potential in intrusion detection, enhancing the adaptability and flexibility of IDSs while reducing false positive rates. However, there are still challenges to be addressed in the application of NITs.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Farid MiarNaeimi, Gholamreza Azizyan, Mohsen Rashki
Summary: This paper introduces a new meta-heuristic algorithm called Horse Herd Optimization Algorithm (HOA), inspired by horses' behavior, which shows excellent performance in high-dimensional optimization problems. By imitating the behavior features of horses at different ages, HOA has a large number of control parameters leading to efficient solving of complex problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mohamed Abdel-Basset, Reda Mohamed, Mohammed Jameel, Mohamed Abouhawwash
Summary: This work presents a novel metaheuristic algorithm called Nutcracker Optimization Algorithm (NOA), inspired by the behaviors of Clark's nutcrackers. NOA mimics the nutcracker's search for seeds and cache storage during summer and fall, as well as its spatial memory strategy during winter and spring. The algorithm is evaluated and compared with other optimization algorithms, demonstrating superior results and ranking first among all methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Changting Zhong, Gang Li, Zeng Meng
Summary: This paper presents a novel swarm-based metaheuristic algorithm called beluga whale optimization (BWO), which is inspired by the behaviors of beluga whales, for solving optimization problems. BWO consists of three phases: exploration, exploitation, and whale fall, corresponding to pair swim, prey, and whale fall behaviors, respectively. The self-adaptive balance factor and probability of whale fall in BWO play significant roles in controlling the exploration and exploitation capabilities. Additionally, Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of BWO is evaluated using 30 benchmark functions and compared with 15 other metaheuristic algorithms through qualitative, quantitative, and scalability analysis. The results show that BWO is a competitive algorithm for solving unimodal and multimodal optimization problems. Furthermore, BWO achieves the first overall rank in the scalability analysis of benchmark functions among the compared metaheuristic algorithms. Four engineering problems are also solved to demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is publicly available.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Shijie Zhao, Tianran Zhang, Shilin Ma, Miao Chen
Summary: This paper proposes a novel swarm intelligence bioinspired optimization algorithm, called the Dandelion Optimizer (DO), that simulates the process of dandelion seed long-distance flight for solving continuous optimization problems. Experimental results indicate that the DO method has outstanding iterative optimization and strong robustness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Chemistry, Analytical
Pavel Trojovsky, Mohammad Dehghani
Summary: This paper introduces a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) to solve optimization problems in various scientific disciplines. By simulating the natural behavior of pelicans during hunting, the POA demonstrates high performance in approaching optimal solutions for unimodal functions and exploring the main optimal area for multimodal functions. Comparison with eight well-known metaheuristic algorithms confirms the competitiveness of POA in providing optimal solutions for optimization problems.
Article
Computer Science, Interdisciplinary Applications
Amir Seyyedabbasi, Farzad Kiani
Summary: The study introduces a new metaheuristic algorithm, SCSO, which mimics the behavior of sand cats. The algorithm performs well in finding good solutions and outperforms compared methods in various test functions and engineering design problems.
ENGINEERING WITH COMPUTERS
(2023)
Review
Computer Science, Interdisciplinary Applications
Manik Sharma, Prableen Kaur
Summary: Meta-heuristics are problem-independent optimization techniques that provide optimal solutions through iterative exploration and exploitation of the entire search space. This study aims to comprehensively analyze the application of nature-inspired meta-heuristics in feature selection through a systematic review of 176 articles.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Gang Hu, Yuxuan Guo, Guo Wei, Laith Abualigah
Summary: This study presents a new nature-inspired metaheuristic algorithm called GKS optimizer (GKSO) based on the behavior of the Genghis Khan shark (GKS). The algorithm simulates the hunting, movement, foraging, and self-protection mechanisms of GKS to achieve efficient optimization in different regions of the search space. The qualitative and quantitative analysis confirms the exploration and exploitation capability of GKSO, and comparative experiments demonstrate its superiority over other algorithms.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Gaurav Dhiman
Summary: The SSC algorithm combines sine-cosine functions and attacking strategy of SHO algorithm to find optimal solutions for complex problems, demonstrating robustness, effectiveness, efficiency, and convergence analysis in comparison with other competitor approaches.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Neetesh Kumar, Navjot Singh, Deo Prakash Vidyarthi
Summary: This study models the dynamic foraging behavior of Redheaded Agama lizards and proposes an artificial lizard search optimization (ALSO) algorithm based on their effective way of capturing prey. The simulation demonstrates the effectiveness of the proposed algorithm over other nature-inspired optimization techniques.
Article
Multidisciplinary Sciences
Slawomir Koziel, Anna Pietrenko-Dabrowska
Summary: This study investigates the benefits of incorporating variable-resolution electromagnetic simulation models into nature-inspired algorithms for optimization of antenna structures. The results show that appropriate resolution adjustment profiles can achieve significant computational savings without noticeable degradation of the search process reliability.
SCIENTIFIC REPORTS
(2023)
Review
Geosciences, Multidisciplinary
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
EARTH-SCIENCE REVIEWS
(2020)
Article
Environmental Sciences
Hossein Hamidifar, Alireza Keshavarzi, Pawel M. Rowinski
Article
Computer Science, Artificial Intelligence
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
SWARM AND EVOLUTIONARY COMPUTATION
(2020)
Article
Environmental Sciences
Gerardo Caroppi, Kaisa Vastila, Paola Gualtieri, Juha Jarvela, Maurizio Giugni, Pawel M. Rowinski
Summary: This study investigated the impacts of embedding natural plant features in the experimental simulation of flow in partly vegetated channels. The results showed that the flexibility-induced mechanisms of natural-like vegetation significantly affected the flow at the interface, leading to deeper vortex penetration and a higher efficiency of lateral momentum transport compared to rigid cylinders.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Emilia Karamuz, Ewa Bogdanowicz, Tesfaye Belay Senbeta, Jaroslaw Jan Napiorkowski, Renata Julita Romanowicz
Summary: The study in the Vistula basin in Poland examined the propagation process from meteorological to hydrological drought, focusing on the relationships between drought indices and identifying factors affecting drought variability induced by natural processes and human interaction. Results showed that the basin's upstream part is mainly influenced by the mining industry, while the middle and downstream parts are additionally affected by industry and agriculture.
Article
Engineering, Civil
Senlin Zhu, Adam P. Piotrowski, Mariusz Ptak, Jaroslaw J. Napiorkowski, Jiangyu Dai, Qingfeng Ji
Summary: The original PSO method used in the air2water model performs relatively poorly compared to most recent algorithms, with only the HARD-DE algorithm consistently outperforming competitors. Therefore, it is highly recommended to use HARD-DE as the calibration method for the air2water model in future studies.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: This study examines the application of neural networks in river temperature modeling, with a focus on the effectiveness of the dropout method in shallow networks. Experimental results show that product unit neural networks with input dropout outperform other models, especially in hilly or mountainous catchments.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Adam P. Piotrowski, Marzena Osuch, Jaroslaw J. Napiorkowski
Summary: This study investigated the impact of climate change on stream temperatures using multiple models in temperate climatic zones of the USA and Poland. The results suggest that stream temperatures are expected to increase due to global warming, with larger warming in different seasons for each region. The study highlights the importance of using multiple temperature models when analyzing the impact of climate change on water temperatures.
JOURNAL OF HYDROLOGY
(2021)
Article
Limnology
Adam P. Piotrowski, Senlin Zhu, Jaroslaw J. Napiorkowski
Summary: The study proposes a simple modification to improve the performance of the air2water model by calibrating a threshold value parameter and tests it on multiple lake datasets.
Article
Environmental Sciences
Hossein Hamidifar, Farzaneh Akbari, Pawel M. Rowinski
Summary: This study investigates the impact of constructing a hydroelectric dam on the natural flow regime and environmental conditions of the Kor River in Iran. The research uses various methods to determine the environmental water requirement of the river and introduces indices to evaluate the allocation of environmental flow in anthropogenic rivers. The results show a deficiency in the allocation of environmental water requirement after the dam construction, leading to the disruption of the river's ecological balance and habitat state.
Article
Engineering, Electrical & Electronic
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Senlin Zhu
Summary: The article proposes an improved air2water model for modeling lake surface water temperature based on air temperature. It distinguishes between cold and warm lake stratification and considers the mixture of both. Compared to classical air2water models, the proposed variant performs better on about 90% of tested lakes, but only with proper calibration approach.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jaroslaw J. J. Napiorkowski, Adam P. P. Piotrowski, Emilia Karamuz, Tesfaye B. B. Senbeta
Summary: This study compared the performance of DE and PSO algorithms in the calibration of conceptual rainfall-runoff models. The results showed that DE algorithms perform better on calibration data, but there are significant differences observed between results obtained for calibration and validation data sets.
Article
Automation & Control Systems
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: This paper compares Particle Swarm Optimization and Differential Evolution, two landmark metaheuristics, and finds that the performance of Differential Evolution algorithms is clearly better than Particle Swarm Optimization ones. Despite being more commonly used in the literature, Particle Swarm Optimization algorithms are outperformed by Differential Evolution on single-objective numerical benchmarks and real-world problems. Therefore, there is a need to reconsider the algorithmic philosophy of Particle Swarm Optimization variants to enhance their competitiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Water Resources
Tesfaye Belay Senbeta, Emila Karamuz, Krzysztof Kochanek, Jaroslaw Jan Napiorkowski, Renata Julita Romanowicz
Summary: The aim of this study is to extend the current knowledge of the Budyko approach in unsteady-state conditions and examine the importance of model structural uncertainties. By using three Budyko-based models (Turc-Pike, Zhang, Fu) with the concept of effective precipitation, we improved the prediction of annual water balance. Additionally, we discussed the significance of considering structural uncertainties in water balance modeling.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Computer Science, Information Systems
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: Hundreds of variants of Swarm Intelligence or Evolutionary Algorithms are proposed each year, but the improvement achieved by these algorithms over Rosenbrock's algorithm is relatively limited, especially for real-world problems.
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)
