Article
Computer Science, Information Systems
Zakir Hussain Ahmed, Naif Al-Otaibi, Abdullah Al-Tameem, Abdul Khader Jilani Saudagar
Summary: This paper studies the use of genetic algorithm (GA) to solve the capacitated vehicle routing problem (CVRP) and compares the performance of different crossover operators. The study finds that distance-based crossover operators outperform blind crossover operators in solving CVRP. The sequential constructive crossover operator with and without mutation operator is determined to be the best for CVRP.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Biology
Mohammed A. Awadallah, Abdelaziz Hammouri, Mohammed Azmi Al-Betar, Malik Shehadeh Braik, Mohamed Abd Elaziz
Summary: This paper introduces a binary version of Horse herd Optimization Algorithm (HOA) to address Feature Selection (FS) problems, making adjustments to improve performance through transfer functions and crossover operators, and evaluating the algorithm using real-world FS datasets, showing competitive results.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Qingwu Fan, Shaoen Wu, Xingqi Zhou, Lanbo Li, Zidong Wang
Summary: This article proposes a real-coded genetic algorithm based on AIDX, which utilizes auxiliary individuals to enhance stability and global search capability for multidimensional optimization problems. Experimental results demonstrate that AIDX-GA shows excellent performance and stability in solving multidimensional optimization problems, making it suitable for a wide range of IoT applications.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Computer Science, Artificial Intelligence
Octavio Ramos-Figueroa, Marcela Quiroz-Castellanos, Efren Mezura-Montes, Rupak Kharel
Summary: This paper reviews the variation operators included in GGAs for solving NP-hard grouping problems, organizing them into three classifications based on variation-degree, solutions encoding, and parameter setting-level, respectively.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Hari Mohan Pandey, Marcello Trovati, Nik Bessis
Summary: This paper introduces a search algorithm based on mask-fill reproduction operators, which uses a rigorous statistical methodology to analyze the performance of genetic algorithms. The impact of different reproduction operator combinations on algorithm performance is compared, with a focus on grammatical inference as the domain of investigation. The proposed algorithm is evaluated against state-of-the-art algorithms using statistical tests to determine performance significance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Ahmed A. Ewees, Marwa A. Gaheen, Zaher Mundher Yaseen, Rania M. Ghoniem
Summary: Feature selection is an important phase in data mining, which improves the efficiency of learning models. Comprehensive and greedy algorithms are not suitable for handling a large number of features, and swarm intelligence algorithms are becoming more popular. This paper proposes a new method, called crossover-salp swarm with grasshopper optimization algorithm (cSG), which integrates different algorithms to enhance its performance and flexibility.
Article
Mathematics
Connor Little, Salimur Choudhury, Ting Hu, Kai Salomaa
Summary: The pickup and delivery problem is significant in our interconnected world as it can lead to cost reduction and time savings. This study utilizes a genetic algorithm to solve the multiobjective capacitated pickup and delivery problem, exploring different operations to find optimal solutions.
Article
Computer Science, Interdisciplinary Applications
Yuxiang Guan, Yuning Chen, Zhongxue Gan, Zhuo Zou, Wenchao Ding, Hongda Zhang, Yi Liu, Chun Ouyang
Summary: This study proposes an improved genetic algorithm, MCO-GA, for solving the hybrid flow shop scheduling problem in collaborative manufacturing. By introducing a new crossover operator, SX, MCO-GA demonstrates superior efficiency and effectiveness compared to other algorithms. Experimental results show that MCO-GA achieves significantly better average results in over 60% of instances and outperforms other algorithms in terms of computation time.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Article
Computer Science, Artificial Intelligence
Tugrul Bayraktar, Filiz Ersoz, Cemalettin Kubat
Summary: The study improved the exploitation and exploration aspects of the ABC algorithm and developed three hybrid ABC algorithms. These algorithms were applied to a SCLP dataset, showing that the joint hybrid ABC algorithm is a promising approach for solving the SCLP problem, with genetic operators being more effective than memory mechanisms.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Software Engineering
Diego Fernandes da Silva, Luiz Fernando Okada, Wesley K. G. Assuncao, Thelma Elita Colanzi
Summary: This paper presents three crossover operators to enhance the search-based optimization of PLA design. The operators were evaluated through quantitative and qualitative studies, showing that they contribute to better feature modularization. The results indicate that combining at least two of the proposed operators achieves better results.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Man Kang, Mingsheng Wang
Summary: This study introduces a new genetic algorithm to improve the properties of S-boxes created by the Feistel structure. The new genetic algorithm generates S-boxes with better properties, enhancing the resistance of encryption systems against boomerang attacks.
Article
Computer Science, Artificial Intelligence
Yu Xue, Haokai Zhu, Jiayu Liang, Adam Slowik
Summary: Feature selection is a crucial pre-processing technique for classification, aiming to enhance classification accuracy by removing irrelevant or redundant features. This study introduces a multi-objective genetic algorithm with an adaptive operator selection mechanism, which effectively addresses high-dimensional feature selection problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Panli Zhang, Jiquan Wang, Zhanwei Tian, Shengzhi Sun, Jianting Li, Jingnan Yang
Summary: In this paper, a genetic algorithm with jumping gene and heuristic operators (GA-JGHO) is proposed to solve the traveling salesman problem. By improving the selection method of the combined fitness function, introducing a bidirectional heuristic crossover operator, designing a combination mutation operator, introducing a jumping gene operator, and adding a unique operator, as well as integrating a local search operator, GA-JGHO performs better in quality stability, accuracy, and convergence speed.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Ahmed A. Ewees, Mohammed A. A. Al-qaness, Laith Abualigah, Diego Oliva, Zakariya Yahya Algamal, Ahmed M. Anter, Rehab Ali Ibrahim, Rania M. Ghoniem, Mohamed Abd Elaziz
Summary: The paper introduces a novel feature selection method called AOAGA, which combines arithmetic optimization algorithm with genetic algorithm. Through experiments with multiple benchmark datasets, the method shows promising performance.
Article
Computer Science, Artificial Intelligence
Haoran Liu, Yanbin Cai, Qianrui Shi, Niantai Wang, Liyue Zhang, Sheng Li, Shaopeng Cui
Summary: This paper proposes a novel method, named BNC-HHO, for Bayesian network structure learning using a discrete Harris hawks optimization algorithm. The method employs the max-min parents and children algorithm, V-structure & log-likelihood function, and neighborhood structures in the initialization phase. It extends the Harris hawk optimization algorithm to the discrete domain by redefining movement strategies and uses adaptive crossover and mutation rates based on the X-conditional cloud. Experiments show that the proposed algorithm outperforms other state-of-the-art algorithms in terms of structure scores and convergence accuracy, making it an effective and feasible method for learning Bayesian network structures.
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)