Article
Computer Science, Theory & Methods
Joshua Peake, Martyn Amos, Nicholas Costen, Giovanni Masala, Huw Lloyd
Summary: This paper presents an improved algorithm for the Virtual Machine Placement (VMP) problem, which significantly improves the solution speed by utilizing parallelization techniques and modern processor technologies. The algorithm achieves solution qualities comparable to or even superior to other nature-inspired methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Biology
Song Yang, Lejing Lou, Wangjia Wang, Jie Li, Xiao Jin, Shijia Wang, Jihao Cai, Fangjun Kuang, Lei Liu, Myriam Hadjouni, Hela Elmannai, Chang Cai
Summary: This paper proposes a new algorithm called SCACO, which combines slime mould foraging behavior and collaborative hunting to improve the convergence accuracy and solution quality of ACOR. It also optimizes the ability of ACO to jump out of local optima using an adaptive collaborative hunting strategy. The performance of SCACO is compared with nine basic algorithms and nine variants, demonstrating its effectiveness in classification prediction for the diagnosis of tuberculous pleural effusion.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Dickson Odhiambo Owuor, Thomas Runkler, Anne Laurent, Joseph Onderi Orero, Edmond Odhiambo Menya
Summary: Gradual pattern extraction is a field in Knowledge Discovery in Databases that aims to map correlations between attributes of a data set as gradual dependencies. In this study, three population-based optimization techniques are investigated to improve the efficiency of mining gradual patterns. The results show that ant colony optimization technique outperforms genetic algorithm and particle swarm optimization in the task of gradual pattern mining.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Abdelrahman Elsaid, Karl Ricanek, Zimeng Lyu, Alexander Ororbia, Travis Desell
Summary: Continuous Ant-based Topology Search (CANTS) is a novel nature-inspired neural architecture search algorithm based on ant colony optimization. It utilizes a continuous search space to automate the design of artificial neural networks, removing the limitation of predetermined structure sizes. By adding an extra dimension for neural synaptic weights, CANTS can optimize both architecture and weights, significantly reducing optimization time while maintaining competitive performance.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Kaili Shao, Ying Song, Bo Wang
Summary: In this paper, a hybrid heuristic task scheduling method combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed to improve user satisfaction and resource efficiency. Experimental results show that the proposed method outperforms several recent works with average improvements of 27.9-65.4% in user satisfaction and 33.8-69.6% in resource efficiency.
Article
Biology
Lei Liu, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Chengye Li, Jinsheng Ouyang, Huiling Chen, Majdi Mafarja, Hamza Turabieh, Jingye Pan
Summary: This study introduces a multilevel COVID-19 X-ray image segmentation method based on ant colony optimization. By improving the algorithm, it effectively enhances the diagnostic level.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biotechnology & Applied Microbiology
Zhang Yi, Zhou Yangkun, Yu Hongda, Wang Hong
Summary: This paper presents an improved Discrete Salp Swarm Algorithm based on the Ant Colony System (DSSACS). The algorithm shows better performance in terms of convergence speed, positive feedback mechanism, and accuracy compared to other algorithms. Moreover, it also achieves shorter paths in the selection of optimal paths in the Wireless rechargeable sensor network (WRSN) problem, saving more time and economic cost.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Sayed A. Mohsin, Saad Mohamed Darwish, Ahmed Younes
Summary: Query optimization is crucial in distributed databases, aiming to reduce query execution costs by finding an optimal join order. Researchers focus on finding suitable algorithms to tackle the NP-hard problem of query processing, especially for large databases. The use of quantum-inspired algorithms, such as QIACO, has shown promise in improving query performance by diversifying search space and avoiding local optima.
Article
Computer Science, Interdisciplinary Applications
Yunlou Qian, Jiaqing Tu, Gang Luo, Ce Sha, Ali Asghar Heidari, Huiling Chen
Summary: This paper investigates the application of remote sensing images in urban surface morphology and geographic conditions, using the multi-threshold image segmentation method for image segmentation research. The performance of the original algorithm is enhanced by introducing salp foraging behavior. The experimental results demonstrate the advantages of SSACO in remote sensing image segmentation.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Biology
Ailiang Qi, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Zongda Wu, Zhennao Cai, Fayadh Alenezi, Romany F. Mansour, Huiling Chen, Mayun Chen
Summary: This paper focuses on the study of COVID-19 X-ray image segmentation technology. A new multilevel image segmentation method based on the swarm intelligence algorithm is proposed, along with a designed image segmentation model. Experimental results show that the proposed model achieves more stable and superior segmentation results at different threshold levels.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Multidisciplinary
Fatma Mbarek, Volodymyr Mosorov
Summary: Dynamic load balancing in distributed computing systems is crucial for system stability, but faces challenges due to the complexity of optimization. Researchers have developed a new hybrid algorithm to address this issue, and experimental results have shown its effectiveness in improving system performance.
APPLIED SCIENCES-BASEL
(2021)
Article
Multidisciplinary Sciences
Sayed A. Mohsin, Ahmed Younes, Saad M. Darwish
Summary: The quantum-inspired ant colony algorithm improves query join costs and efficiency in distributed databases by diversifying and expanding search spaces, speeding up convergence, and avoiding local optima. Experiments show faster convergence and better results compared to classic models.
Article
Computer Science, Information Systems
Hamed Tabrizchi, Marjan Kuchaki Rafsanjani
Summary: Cloud computing has become a significant domain of processing service in recent years, with one of the main problems being the placement of virtual servers onto physical servers. This paper proposes an approach using ant colony algorithm to optimize the allocation of resources to VMs, aiming to minimize the impact on the environment and energy consumption while improving the energy efficiency of physical servers.
JOURNAL OF GRID COMPUTING
(2021)
Article
Computer Science, Information Systems
Arun Kumar Sangaiah, Amir Javadpour, Forough Ja'fari, Pedro Pinto, Weizhe Zhang, Sudha Balasubramanian
Summary: In cloud computing environments, the use of Intrusion Detection Systems (IDSs) is crucial for tackling security challenges. This study focuses on finding an effective feature selection method to enhance the accuracy of intrusion detection classifiers. The proposed Hybrid Ant-Bee Colony Optimization (HABCO) method is utilized to convert the feature selection problem into an optimization problem, and it is found to outperform other mentioned methods in terms of accuracy.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Claudia Campolo, Antonio Iera, Antonella Molinaro
Summary: To keep up with the rapid growth of AI and ML applications, distributed intelligence solutions are gaining momentum and utilizing cloud facilities, edge nodes, and end-devices to enhance computational power, meet application requirements, and optimize performance. However, distributing intelligence throughout the cloud-to-things continuum poses unprecedented challenges to network design. This paper explores the distributed intelligence scenario and critically analyzes existing research achievements, identifies key building blocks of a network ecosystem for distributed intelligence, and provides design guidelines based on the authors' perspectives.