Article
Engineering, Chemical
Rui Liu, Yuanbin Mo
Summary: The study proposed an enhanced version of the sparrow search algorithm to optimize the coverage of wireless sensor networks. Through numerical tests, the enhanced algorithm demonstrated faster convergence to the optimum solution and advantages in convergence speed, robustness, and anti-local extremum ability.
Article
Computer Science, Information Systems
Krishnamoorthy Suresh, Ali Alqahtani, Thangaraj Rajasekaran, Murugan Suresh Kumar, Venugopal Ranjith, Raju Kannadasan, Nayef Alqahtani, Arfat Ahmad Khan
Summary: Mobile operators need to invest more in network infrastructures to keep up with the growth of the internet and technological advancements. Cloud-RAN and software defined networking are considered promising technologies for reducing costs and improving scalability in 5G networks. This study proposes an algorithm for self-optimizing networks by analyzing quality-of-service information to select the best combination of baseband and remote radio head units, reducing call blocking and optimizing network balance. Evaluation results show that the proposed algorithm outperforms existing algorithms in terms of blocking probability, throughput, and response time.
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
Chemistry, Multidisciplinary
Dan-Dan Yang, Meng Mei, Yu-Jun Zhu, Xin He, Yong Xu, Wei Wu
Summary: The enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) proposed in this paper optimizes network coverage, node utilization, and network energy balance objectives effectively, improving coverage optimization in wireless sensor networks in complex environments.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Anis Jari, Avid Avokh
Summary: This paper introduces two algorithms to address the issues in multi-sink wireless sensor networks, aiming to improve network lifetime and reduce energy consumption through enhanced sink placement and anycast routing.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Farzad Kiani, Fateme Aysin Anka, Fahri Erenel
Summary: This study proposes a new method called PSCSO, inspired by the political system, to improve the SCSO algorithm. It increases the chances of finding the global solution by randomly choosing positions between the candidate's current position and the best solution found so far.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Computer Science, Hardware & Architecture
M. Shyama, Anju S. Pillai, Alagan Anpalagan
Summary: This paper introduces a fault-tolerant routing path identification method with genetical swarm optimization (FTGSO) in wireless sensor networks (WSN). The method selects cluster head (CH) nodes using genetical swarm optimization and employs a self-healing method to resolve network connectivity issues. Experimental results demonstrate that the proposed method achieves better performance compared to other existing routing protocols.
Article
Physics, Multidisciplinary
Thi-Kien Dao, Shu-Chuan Chu, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Nguyen
Summary: This paper proposes a solution to the optimal node coverage of unbalanced wireless sensor network distribution during random deployment based on an enhanced Archimedes optimization algorithm. The algorithm effectively improves the feasible range and convergence speed by combining the best network coverage results from multiple sub-areas.
Article
Chemistry, Multidisciplinary
Hongmei Fei, Baitao Zhang, Yan Liu, Manli Yan, Yi Lu, Jie Zhou
Summary: In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has led to the development of innovative task allocation algorithms. These algorithms serve as efficient stochastic optimization techniques aimed at maximizing revenue for the network's objectives. To tackle the computational challenges caused by the increase in sensor numbers, this paper introduces the Chaotic Elite Adaptive Genetic Algorithm (CEAGA), which outperforms other methods in terms of task allocation performance in IUWSNs.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Lalit Kumar, Manish Pandey, Mitul Kumar Ahirwal
Summary: The computational time of swarm optimization algorithms, including Particle Swarm Optimization (PSO), is increased due to the large number of decision variables in complex problems. A new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm, combining PSO and Jaya algorithm, is proposed to provide a more parallel version of the algorithm. The proposed algorithm outperforms other parallel PSO versions and Jaya algorithm in terms of computational time and optimal solution.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Pakarat Musikawan, Yanika Kongsorot, Paisarn Muneesawang, Chakchai So-In
Summary: This paper introduces an improved competitive swarm optimizer that incorporates virtual force algorithm and Voronoi diagram to enhance the coverage performance and energy efficiency of wireless sensor networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Mohamed Elshrkawey, Hassan Al-Mahdi, Walid Atwa
Summary: This paper proposes a routing algorithm based on a novel RPSO algorithm and a new fitness function. The evaluation and comparison results demonstrate that this algorithm outperforms others in terms of convergence speed and global optimum identification, and it successfully improves performance metrics such as network lifetime, energy consumption, and data throughput in wireless sensor networks.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Yamin Han, Bo Yang, Heejung Byun
Summary: This study optimizes the deployment of actuators in wireless sensor networks using the hierarchical intermittent communication particle swarm optimization (HICPSO) method, considering the coverage rate of actuators to sensor nodes and the energy consumption rate of sensor nodes as optimization goals to balance energy consumption among sensor nodes and solve energy hole problem. The proposed method effectively increases the coverage rate of actuators to sensor nodes, reduces the energy consumption rate of the sensor nodes, and reduces the packet drop ratio, showing improved performance compared to traditional methods.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Sen Yang, Linbo Zhang, Xuesen Yang, Jiayun Sun, Wenhao Dong
Summary: The paper proposes an improved arithmetic optimization algorithm, ASFAOA, which enhances the performance of the original algorithm in terms of local exploitation and global exploration capability. Experimental results demonstrate that ASFAOA outperforms the original algorithm and other comparison algorithms in test function evaluation and wireless sensor coverage problem solving.
Article
Mathematics
Akram Belazi, Hector Migallon, Daniel Gonzalez-Sanchez, Jorge Gonzalez-Garcia, Antonio Jimeno-Morenilla, Jose-Luis Sanchez-Romero
Summary: This paper introduces an enhanced version of the sine cosine algorithm (ESCA algorithm) and designs several parallel algorithms to improve solution accuracy and convergence speed. Experimental results demonstrate the superiority of the proposed algorithm and its outstanding performance in engineering design problems. Additionally, the overall performance of the algorithm is statistically validated using non-parametric statistical tests.
Article
Computer Science, Artificial Intelligence
Nengxian Liu, Jeng-Shyang Pan, Shu-Chuan Chu, Taotao Lai
Summary: This article introduces an efficient surrogate-assisted bi-swarm evolutionary algorithm (SABEA) with hybrid and ensemble strategies for computationally expensive optimization problems. The proposed SABEA combines differential evolution (DE) and teaching-learning-based optimization (TLBO) to achieve strong exploration and exploitation capabilities. Moreover, the cooperation of global and local surrogate models effectively estimates the fitness value. Experimental results demonstrate the superior performance of SABEA compared to state-of-the-art competing algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Lu-Lu Liang, Shu-Chuan Chu, Zhi-Gang Du, Jeng-Shyang Pan
Summary: This paper proposes a two-layer Surrogate-Assisted Phasmatodea Population Evolution (SAPPE) algorithm for 3D coverage of wireless sensors by combining the characteristics of meta-heuristic algorithms and surrogate models. The algorithm shows good performance in terms of node quantity and coverage radius through experiments and analysis.
Article
Computer Science, Artificial Intelligence
Vaclav Snasel, Rizk M. Rizk-Allah, Davut Izci, Serdar Ekinci
Summary: This paper proposes a powerful integrated optimization algorithm, INFO-GBB, for determining the optimal parameters of a power system stabilizer (PSS) model used in a single-machine infinite-bus (SMIB) system. By combining INFO optimizer with COBL and GBB strategies, INFO-GBB algorithm enhances the searching capability and solution diversity. The effectiveness of the algorithm is validated on CEC 2020 benchmark suits, and the results show superior performance compared to other algorithms. Therefore, INFO-GBB algorithm can efficiently handle the parameter estimation and function optimization tasks of the PSS model.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Jie Shan, Bo-Lin Xie, Yong-Jun Zhang, Jeng-Shyang Pan, Yu-Hong Xie, Yang Fu
Summary: This paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA), which improves the convergence rate and solution accuracy by splitting the initial population into subgroups and exchanging information among them. The Taguchi method is adopted as a communication strategy in the parallelization technique, enhancing the robustness and accuracy of the solution. Experimental results show that PTSSA is more competitive than common algorithms and it is also applied to optimize the operation of a combined cooling-power system, providing stable and efficient cost reduction.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Article
Computer Science, Information Systems
Shu-Chuan Chu, Qing Feng, Jia Zhao, Jeng-Shyang Pan
Summary: The heuristic optimization algorithm is a popular method for solving optimization problems, but it suffers from unstable performance which is heavily dependent on problem specifics and the designer's experience. In this paper, a new meta-heuristic algorithm called Bamboo Forest Growth Optimization (BFGO) is proposed. The BFGO algorithm incorporates the growth law of bamboo and the optimization process, showing better performance compared to other algorithms when applied to various optimization problems.
JOURNAL OF INTERNET TECHNOLOGY
(2023)
Article
Computer Science, Theory & Methods
ZhiSheng Wang, Shu-Chuan Chu, JianPo Li, Jeng-Shyang Pan
Summary: In this paper, an energy-adaptive clustering method based on Taguchi-based-GWO optimizer (EACM-TGWO) is proposed for wireless sensor networks with a mobile sink. The method determines the optimal number of cluster heads (CHs) based on the energy consumption characteristics of the network and uses a fitness function to select CHs. The Taguchi-based grey wolf optimizer (TGWO) algorithm is employed to search for the optimal set of CHs. Simulation results demonstrate that EACM-TGWO outperforms other algorithms in terms of balancing energy consumption and saving network energy.
Article
Automation & Control Systems
Quang-Thinh Bui, My-Phuong Ngo, Vaclav Snasel, Witold Pedrycz, Bay Vo
Summary: The neutrosophic fuzzy set (NF-set) is a hybrid structure combining fuzzy and neutrosophic sets, used to handle imprecise or vague data. This paper introduces similarity-based information measures, including entropy and cross-entropy, between NF-sets for the first time. The paper also proposes an efficient algorithm for multi-criteria decision making using these information measures.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Lingping Kong, Seyedali Mirjalili, Vaclav Snasel, Jeng-Shyang Pan, Akshaya Raj, Radana Vilimkova Kahankova, Martinek Radek
Summary: Metaheuristic algorithms (MAs) are widely used in optimization, including non-invasive fetal electrocardiogram (fECG) extraction. This paper investigates the impact of hyperparameters on the performance of MAs and proposes a framework for fECG extraction. The experimental results show that the performance of MAs can be influenced by hyperparameters, and some algorithms may not be suitable for certain problems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Chia-Cheng Hu, Wen -Wu Liu, Jeng-Shyang Pan
Summary: This paper addresses the problem of content distribution in C-RANs with the aim of minimizing the total traffic cost of content transmission. The problem is formulated as a mixed-integer linear programming and an algorithm with a bounded approximation ratio is proposed. Simulation results demonstrate the superiority of the proposed algorithm compared to recent heuristic algorithms and show that the difference between the algorithm's solution and the optimal solution is very small. The proposed algorithm for content exchange between neighbor RRHs is also shown to be feasible in C-RANs.
Article
Computer Science, Information Systems
Lingping Kong, Varun Ojha, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, Vaclav Snasel
Summary: This study proposes a Global Representation (GR) based attention mechanism to alleviate the heterophily and over-smoothing issues. The model integrates geometric information and uses GR to construct the Key, discovering the relation between nodes and the structural representation of the graph. Experimental tests validate the performance of the proposed method and provide insights for future improvements.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Van-Vang Le, Phu Pham, Vaclav Snasel, Unil Yun, Bay Vo
Summary: There are various types of information networks that have gained popularity in recent years, including social networks, citation networks, and email communication networks. Network alignment, which aims to match users with the same identification across different networks, is a well-researched topic due to its potential real-world applications. However, existing anchor link prediction methods still struggle with preserving the global graph-structured features of individual networks, resulting in subpar prediction results. To address this challenge, we propose a novel model that combines four embedding techniques to align users between information networks using a seed set of known anchor links. We evaluate the effectiveness of our approach through comprehensive experiments on real-life network alignment datasets and compare it with state-of-the-art baseline methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiaopeng Wang, Shu-Chuan Chu, Vaclav Snasel, Hisham A. Shehadeh, Jeng-Shyang Pan
Summary: This paper presents a new meta-heuristic algorithm called the five phases algorithm (FPA), which is inspired by the five phases theory in traditional Chinese thought. FPA updates agents based on the generating and overcoming strategy as well as learning strategy from the agent with the same label. FPA has a simple structure but excellent performance, and it requires only two general parameters.
JOURNAL OF INTERNET TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Shu-Chuan Chu, Zhi-Yuan Shao, Ning Zhong, Geng-Geng Liu, Jeng-Shyang Pan
Summary: Mobile sensors are being used more frequently in real-life applications as they can extend monitoring range and overcome limitations of static sensors. This paper improves the Monte Carlo Localization (MCL) algorithm by enhancing the food digestion algorithm (FDA) used in the localization of mobile sensors to reduce errors and improve accuracy. The paper proposes three inter-group communication strategies based on the topology between groups to accelerate the convergence of the algorithm, and the improved algorithm achieves good localization results.
Article
Computer Science, Artificial Intelligence
Zi-Ming Wu, Tao Liu, Bin Yan, Jeng-Shyang Pan, Hong-Mei Yang
Summary: This paper proposes a hardware architecture to accelerate the generation of shares and reconstruction of the secret, achieving more than ten times faster secret sharing than software implementation. It enables secret sharing of 3D data cubes and provides preliminary tools for multi-party computation.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2023)
Article
Operations Research & Management Science
Hala A. Omar, Vaclav Snasel, Rizk M. Rizk-Allah
Summary: This paper proposes a RSA-LM-Haar algorithm based on Haar wavelets to efficiently solve regular and singular BVPs. The performance of the algorithm is evaluated through case studies and compared with the LM-Haar algorithm, showing remarkable results and capabilities in solving various BVPs.
