Article
Computer Science, Artificial Intelligence
Omur Sahin, Bahriye Akay
Summary: Microservices decompose applications into maintainable services and reduce complexity. The study proposes a Discrete Dynamic Artificial Bee Colony with Hyper-Scout algorithm to address issues in RESTful testing generation. Experimental results show the algorithm achieved high performance in multiple problems.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Omur Sahin, Bahriye Akay, Dervis Karaboga
Summary: Testing object-oriented software is challenging due to various properties like classes, inheritance, states, behavior, association, and polymorphism. Search-based testing methods like ABC algorithm can automatically generate test cases to optimize coverage goals. Use of archive in ABC algorithm improves convergence and coverage for software testing.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Automation & Control Systems
Serap Ercan Comert, Harun Resit Yazgan
Summary: This paper introduces three multi-objective electric vehicle routing problems that consider different charging strategies and electric vehicle charger types while optimizing five conflicting objectives. A new hierarchical approach consisting of Hybrid Ant Colony Optimization (HACO) and Artificial Bee Colony Algorithm (ABCA) is developed to solve these problems. The proposed approach is examined on test-based instances and achieves the best new results in most cases.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Mechanical
Yibing Cui, Wei Hu, Ahmed Rahmani
Summary: The improved ABC algorithm proposed in this paper adjusts the number of employed bees and onlooker bees, enhances the diversity of initial population, and utilizes self-adaptive parameters in differential search equations, which leads to better solution quality and convergence speed compared to competitors.
NONLINEAR DYNAMICS
(2022)
Article
Mechanics
Hashem Jahangir, Danial Rezazadeh Eidgahee
Summary: The study used machine learning approaches to estimate bond strength, showing that the proposed ABC-ANN model outperforms existing models in terms of accuracy and robustness.
COMPOSITE STRUCTURES
(2021)
Article
Computer Science, Artificial Intelligence
Yuyang Bai, Changsheng Zhang, Weitong Bai
Summary: The article introduces a two-level parallel decomposition-based artificial bee colony algorithm for solving dynamic multiple-objective optimization problems. By decomposing the problem into a set of single-objective optimization problems and using an improved parallel bee colony algorithm for solving them, the method can efficiently obtain the Pareto front and shows good performance in experiments.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Yuan-Zhen Li, Kaizhou Gao, Lei-Lei Meng, Ponnuthurai Nagaratnam Suganthan
Summary: This work addresses the distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption. An improved artificial bee colony (IABC) algorithm is proposed to solve the problem, utilizing new solution generation operators and a local search operation. Experimental results show that the IABC algorithm performs well in solving the DPFSP with peak power consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematics
Nien-Che Yang, Danish Mehmood, Kai-You Lai
Summary: This study proposes a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm for achieving optimal design of passive power filters. Through a series of case studies, the efficiency and better performance of the proposed method over previous well-known algorithms have been demonstrated.
Review
Automation & Control Systems
Ebubekir Kaya, Beyza Gorkemli, Bahriye Akay, Dervis Karaboga
Summary: The ABC algorithm is a popular optimization algorithm that has been successfully applied to solve real-world problems. This study examines combinatorial optimization approaches based on the ABC algorithm, provides summaries of related studies, and introduces the ABC algorithm-based approaches used. The study also evaluates mechanisms to improve the local search capability of the ABC algorithm and analyzes neighborhood operators, selection schemes, initial populations determination approaches, hybrid approaches, and test instances used in evaluating the performances of ABC algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Xing Ren, Hongwei Zhang
Summary: The improved Artificial Bee Colony (ABC) algorithm proposed in this article can develop a novel ANC algorithm without the need for secondary path modeling. This algorithm features global optimization ability and anti-interference ability, with faster convergence rate and better noise reduction performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Mechanical
Fucheng Han, Xin Li, Shengwenjun Qi, Wenhua Wang, Wei Shi
Summary: This study conducted a reliability analysis of wind turbine subassemblies using the three-parameter (3-P) Weibull distribution model based on field data. An improved ergodic artificial bee colony algorithm (ErgoABC) was proposed for the maximum likelihood estimation of the Weibull distribution parameters. The results showed that the 3-P Weibull model can reasonably evaluate the lifetime distribution of critical wind turbine subassemblies.
PROBABILISTIC ENGINEERING MECHANICS
(2023)
Article
Mathematics, Applied
Yulin Deng, Hongfeng Xu, Jie Wu
Summary: This study investigates risk reduction methods of asset securitization using various approaches, including portfolio methods and blockchain technology, to enhance investment security. It proposes an improved ABC algorithm for portfolio optimization and demonstrates its capability to simultaneously optimize multiple features in the investment portfolio, thereby reducing investor errors and improving the balance between investment returns and risks.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Yue Xu, Xiuli Wang
Summary: This paper proposes a two-stage approach to solve the staff scheduling problem in call centers. The approach utilizes the artificial bee colony algorithm and integer programming to generate and optimize shift schedules. Experimental results demonstrate the effectiveness and efficiency of the proposed method in providing good solutions for large-scale problems. Additionally, guidance is provided on balancing employees' working preferences and labor costs with staff satisfaction.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Jianchun Zhang, Lei Li, Zhiwei Chen
Summary: This paper introduces a newly-defined reliability-based strength-redundancy allocation problem (SRAP) for multi-state systems and improves the artificial bee colony (ABC) algorithm to solve the reliability optimization design model, aiming to enhance the system reliability.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Alireza Etminaniesfahani, Hanyu Gu, Amir Salehipour
Summary: The artificial bee colony (ABC) is a simple, flexible, and efficient metaheuristic optimization algorithm, but it suffers from slow convergence due to a lack of powerful local search capability. This paper proposes hybridizing ABC with the Fibonacci indicator algorithm (FIA) to achieve strong exploration and highly efficient exploitation capabilities, and it shows superior performance in various optimization functions.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Automation & Control Systems
Hojjat Rakhshani, Effat Dehghanian, Amin Rahati
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2016)
Article
Biochemistry & Molecular Biology
Hojjat Rakhshani, Effat Dehghanian, Amin Rahati
JOURNAL OF MOLECULAR MODELING
(2019)
Article
Computer Science, Artificial Intelligence
Kazem Talaei, Amin Rahati, Lhassane Idoumghar
APPLIED SOFT COMPUTING
(2020)
Article
Chemistry, Multidisciplinary
E. Shamsi, A. Rahati, E. Dehghanian
Summary: This study introduces a method that combines PSO and machine learning algorithms to build QSAR models for predicting the activity of inhibitors for AChE and BuChE enzymes. It utilizes transfer functions and concepts like catfish effect and chaotic map to enhance the exploration ability in searching for an optimal subset of descriptors, and then validates the best models using statistical methods and machine learning algorithms.
SAR AND QSAR IN ENVIRONMENTAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Amin Rahati, Esmaeil Mirkazehi Rigi, Lhassane Idoumghar, Mathieu Brevilliers
Summary: This paper proposes two ensemble strategies for the backtracking search algorithm (BSA), one to balance exploration and exploitation abilities and the other to provide diverse search moves with various search step lengths. In addition, a strategy to reinitialize specific individuals of the population is used. The proposed algorithm outperforms nineteen state-of-the-art algorithms in solving 29 problems and achieves at least comparable or better results than the best existing algorithms for engineering design optimization problems.
APPLIED SOFT COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Hojjat Rakhshani, Lhassane Idoumghar, Julien Lepagnot, Mathieu Brevilliers, Amin Rahati
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2018)
Article
Neurosciences
Fateme Maleki, Majid Yousefikhoshbakht, Amin Rahati
BRAIN-BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
(2017)
Article
Computer Science, Artificial Intelligence
Hojjat Rakhshani, Amin Rahati
APPLIED SOFT COMPUTING
(2017)
Article
Multidisciplinary Sciences
Hojjat Rakhshani, Amin Rahati
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Amin Rahati, Hojjat Rakhshani
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2016)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hojjat Rakhshani, Amin Rahati, Effat Dehghanian
2015 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED ENGINEERING AND INNOVATION (KBEI)
(2015)
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)