Article
Automation & Control Systems
Jakub Kudela
Summary: This commentary discusses the issues with two recently developed metaheuristic algorithms, the Sooty Tern Optimization Algorithm and the Tunicate Swarm Algorithm. Both algorithms claim computational superiority over other methods based on experimental results, but this claim is invalid. The algorithms use a zero-bias operator, and many benchmark functions where they excel have optimal solutions located in the zero vector. Furthermore, the provided codes for these methods do not achieve the reported results.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: Metaheuristics, especially the African Vultures Optimization Algorithm (AVOA), play a crucial role in solving optimization problems, outperforming existing algorithms in standard benchmarks and engineering design problems. The statistical evaluation further confirms the significant superiority of AVOA.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Yongliang Yuan, Qianlong Shen, Shuo Wang, Jianji Ren, Donghao Yang, Qingkang Yang, Junkai Fan, Xiaokai Mu
Summary: In this paper, a COVID-19 prevention-inspired bionic optimization algorithm called Coronavirus Mask Protection Algorithm (CMPA) is proposed. The CMPA is based on the infection and immunity process of COVID-19 and simulates self-protection behavior mathematically to offer an optimization algorithm. The performance of CMPA is evaluated and compared to other metaheuristic optimizers, demonstrating its competitiveness. Furthermore, CMPA is applied to identify parameters of a gantry crane's main girder, resulting in improvements in mass and deflection.
JOURNAL OF BIONIC ENGINEERING
(2023)
Review
Food Science & Technology
Tanmay Sarkar, Molla Salauddin, Alok Mukherjee, Mohammad Ali Shariati, Maksim Rebezov, Lyudmila Tretyak, Mirian Pateiro, Jose M. Lorenzo
Summary: Bio-inspired optimization techniques are part of intelligent computing techniques and have wide applications in the field of food processing technology. They can efficiently simulate and solve optimization problems.
CURRENT RESEARCH IN FOOD SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Hoda Zamani, Mohammad H. Nadimi-Shahraki, Amir H. Gandomi
Summary: This paper presents a novel bio-inspired algorithm called SMO, which mimics the behaviors of starlings during their stunning murmuration, to solve complex engineering optimization problems. The SMO introduces dynamic multi-flock construction and three new search strategies, achieving competitive results in solution quality and convergence rate compared to other state-of-the-art algorithms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz, Salima Ouadfel, Ahmed A. Abd El-Latif, Rehab Ali Ibrahim
Summary: Feature selection plays a crucial role in machine learning, and the traditional methods have limitations, prompting the introduction of metaheuristic techniques. The improved IAOS algorithm utilizes a global search strategy and operators to enhance exploration ability, while OBL and SBS enhance the algorithm's performance.
COGNITIVE COMPUTATION
(2022)
Article
Biology
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee, Noha F. Mahmoud, Marwa M. Emam
Summary: This paper introduces a new bio-inspired optimization algorithm called the Liver Cancer Algorithm (LCA), which provides efficient search and exploration methods by simulating the growth and spread of liver tumors. Experimental results show that the LCA algorithm outperforms other methods in handling mathematical benchmark problems and feature selection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Multidisciplinary
Weiguo Zhao, Liying Wang, Seyedali Mirjalili
Summary: The artificial hummingbird algorithm (AHA) proposed in this work mimics the flight skills and foraging strategies of hummingbirds in nature, demonstrating superior competitiveness and effectiveness compared to other meta-heuristic algorithms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Faten Khalid Karim, Doaa Sami Khafaga, Marwa M. Eid, S. K. Towfek, Hend K. Alkahtani
Summary: Wind patterns can be affected by climate change, leading to an increase in storms, hurricanes, and calm periods. These changes have a significant impact on wind power system performance and predictability. To address this, researchers and practitioners are developing advanced algorithms that utilize numerical weather prediction models, machine learning techniques, and real-time meteorological data.
Article
Multidisciplinary Sciences
Renyun Liu, Ning Zhou, Yifei Yao, Fanhua Yu
Summary: The paper proposes a new bio-inspired Aphids Optimization Algorithm (AOA) that simulates the foraging process of aphids with wings, and experiments show that the desired AOA is more efficient than other metaheuristic algorithms.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics
Hernan Peraza-Vazquez, Adrian Pena-Delgado, Prakash Ranjan, Chetan Barde, Arvind Choubey, Ana Beatriz Morales-Cepeda
Summary: This paper proposes a new meta-heuristic algorithm called JSOA, which mimics the hunting behavior of jumping spiders and solves global optimization problems with a fine balance between exploitation and exploration.
Article
Automation & Control Systems
Liying Wang, Qingjiao Cao, Zhenxing Zhang, Seyedali Mirjalili, Weiguo Zhao
Summary: This paper proposes a new bio-inspired meta-heuristic algorithm called artificial rabbits optimization (ARO), which is inspired by the survival strategies of rabbits in nature. ARO algorithm is developed by mathematically modeling these survival strategies to create a new optimizer. The effectiveness of ARO is tested and compared with other optimizers, showing superior performance in solving benchmark functions and engineering problems. Moreover, ARO is applied to the fault diagnosis of a rolling bearing, demonstrating its practicality in solving real-world problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Nitish Chopra, Muhammad Mohsin Ansari
Summary: The Golden Jackal Optimization (GJO) algorithm, inspired by the hunting behavior of golden jackals, utilizes prey searching, enclosing, and pouncing steps mathematically to solve challenging engineering problems with unidentified search spaces.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Aguila-Leon, Carlos Vargas-Salgado, Cristian Chinas-Palaciosa, Dacil Diaz-Bello
Summary: This study proposes a Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm, which outperforms traditional techniques in various test scenarios, with higher output power, efficiency, and faster response time.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Energy & Fuels
Liming Sun, Jingbo Wang, Lan Tang
Summary: The study introduces a novel bio-inspired grouped beetle antennae search (GBAS) algorithm to identify unknown parameters of three different PV models. The optimization efficiency of GBAS algorithm is significantly enhanced through a cooperative searching group, and a dynamic balance mechanism is designed between local exploitation and global exploration to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that the GBAS algorithm outperforms other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)