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
Computer Science, Artificial Intelligence
Chenjun Tang, Wei Sun, Min Xue, Xing Zhang, Hongwei Tang, Wei Wu
Summary: This paper improves the defects and deficiencies of the recently proposed whale optimization algorithm (WOA) by proposing an artificial bee colony mixed whale optimization algorithm (ACWOA). The ACWOA algorithm integrates the artificial bee colony algorithm and chaotic mapping to avoid local optima and improve the quality of the initial solution. Nonlinear convergence factors and adaptive inertia weight coefficients are added to accelerate the convergence rate. Experimental results demonstrate the competitiveness of the ACWOA algorithm in terms of convergence speed and solution accuracy.
Article
Computer Science, Artificial Intelligence
Yufang Wang, Jiarong Ge, Sheng Miao, Tianhua Jiang, Xiaoning Shen
Summary: This study proposes a new hybrid algorithm that incorporates machine load rate and heuristic strategies to improve efficiency and solution diversity in solving multi-objective flexible job-shop scheduling problems. Experimental results demonstrate the effectiveness of the proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Computer Science, Software Engineering
Kolluri Rajesh, Sumanta Pyne
Summary: Scheduling operations for DMFB is a multi-constrained optimization problem, and we propose a hybrid artificial bee colony algorithm using generalized N-point crossover to tackle this issue. Our algorithm produces a higher number of optimal solutions in shorter execution times compared to existing algorithms.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(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
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
Computer Science, Information Systems
Weicun Zhang, Yanan Li
Summary: A many-objective artificial bee colony algorithm based on adaptive grid (MOABCAG) is proposed to enhance solution convergence and diversity by improving the location sharing mechanism and setting an adaptive grid search method. Comparing with other algorithms, MOABCAG shows better performance in solving many-objective optimization problems.
Article
Computer Science, Information Systems
Toshiyuki Satoh, Shun Nishizawa, Jun-ya Nagase, Naoki Saito, Norihiko Saga
Summary: This paper addresses the design problem of discrete-time stable unknown input estimators (UIEs) using the artificial bee colony (ABC) algorithm for parameter optimization. A stability-guaranteed design method is presented along with a new objective function incorporating waveform-based and norm-based performance criteria to improve disturbance rejection properties. The proposed method is compared to a previous method based on estimated disturbances to confirm the improvement in disturbance rejection properties at the plant output. Additionally, a constrained ABC algorithm is combined with the original UIE design method and compared in terms of disturbance rejection properties.
INFORMATION SCIENCES
(2023)
Article
Engineering, Civil
Farqad K. J. Jawad, Celal Ozturk, Dansheng Wang, Mohammed Mahmood, Osama Al-Azzawi, Anas Al-Jemely
Summary: In this study, the ABC algorithm is utilized for combined optimization of truss structures, optimizing layout and member size while considering stress and buckling constraints. The results show the superiority of ABC over other algorithms in terms of optimized weight and structural analyses, with a robust performance and 100% success rate demonstrated across four benchmark optimization problems.
Article
Computer Science, Theory & Methods
Tianhua Li, Yongcheng Yin, Bo Yang, Jialin Hou, Kai Zhou
Summary: The paper introduces a self-learning artificial bee colony genetic algorithm (SLABC-GA) based on reinforcement learning to solve cloud service composition and optimization (CSCO) problems, with faster speed, greater precision, and higher stability. The algorithm aims to avoid local optima and improve the precision of the traditional artificial bee colony algorithm (ABC), with a genetic algorithm (GA) introduced later for further accuracy and convergence speed enhancements. Through comparative experiments, the SLABC-GA outperforms GA and ABC in terms of accuracy and speed for large-scale CSCO problems.
Article
Computer Science, Artificial Intelligence
Elif Deniz Yelmenoglu, Numan Celebi, Tugrul Tasci
Summary: This paper presents a novel unsupervised hybrid optimization method for saliency detection. The method combines artificial bee colony and firefly algorithms to provide accurate and fast solutions. By using a superpixel-based method to extract salient regions, the proposed method demonstrates superior performance in terms of accuracy, precision, and speed.
PATTERN ANALYSIS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Yang Yang, Desheng Liu
Summary: In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address the core problem of imaging satellite mission planning. The algorithm improves upon the basic artificial bee colony algorithm and shows good performance in simulation experiments.
Article
Mathematics
Ebubekir Kaya
Summary: This study proposes a new neural network training algorithm called HABCES, which achieves better performance in terms of solution quality and convergence speed. By making fundamental changes to the traditional algorithms, HABCES shows improvements in global optimization problems and ANN training.
Article
Computer Science, Information Systems
Jiaxu Ning, Haitong Zhao, Chang Liu
Summary: An improved exhausted food source identification mechanism based on space partitioning is designed to address the issue of inefficient exploration and excessive searching resources allocation in existing ABC algorithms. The mechanism is applied to both the basic ABC algorithm and a recently improved version, showing better performance in almost all functions on the CEC2015 test suit compared to the original ABC algorithms.
Article
Computer Science, Artificial Intelligence
Hammoudi Abderazek, Ali Riza Yildiz, Seyedali Mirjalili
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Materials Science, Characterization & Testing
Betul Sultan Yildiz, Ali Riza Yildiz, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, Vivek Patel
Article
Materials Science, Characterization & Testing
Dies Kurtulus, Ali Riza Yildiz, Sadiq M. Sait, Sujin Bureerat, Khon Kaen
Article
Materials Science, Characterization & Testing
Ayhan Balkan, Ali Riza Yildiz, Sadiq M. Sait, Sujin Bureerat
Article
Engineering, Aerospace
Pakin Champasak, Natee Panagant, Nantiwat Pholdee, Sujin Bureerat, Ali Riza Yildiz
AEROSPACE SCIENCE AND TECHNOLOGY
(2020)
Review
Computer Science, Interdisciplinary Applications
Zeng Meng, Gang Li, Xuan Wang, Sadiq M. Sait, Ali Riza Yildiz
Summary: Reliability-based design optimization (RBDO) is an excellent method for balancing economy and safety, but challenges such as global convergence capacity and complex design variables hinder its wider application. This study focuses on applying metaheuristic algorithms to RBDO for improved global convergence, robustness, accuracy, and computational speed, highlighting the differences between metaheuristic and gradient algorithms.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Betul Sultan Yildiz, Nantiwat Pholdee, Sujin Bureerat, Ali Riza Yildiz, Sadiq M. Sait
Summary: The study focuses on the design of a robot gripper mechanism and proposes a new optimization method based on the grasshopper optimization algorithm and Nelder-Mead algorithm. By solving real-world engineering problems, the advantages of HGOANM are demonstrated.
Article
Engineering, Mechanical
Emre Isa Albak, Erol Solmaz, Ali Riza Yildiz, Ferruh Ozturk
Summary: Inspired by the mechanical properties of graphene, the study focused on the design of graphene type multi-cell tubes. The best model, GTMT5, was determined using COPRAS and multiobjective optimization methods, showing that circular structures in multi-cell tubes have a significant impact on crashworthiness performance.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2021)
Correction
Engineering, Mechanical
Emre Isa Albak, Erol Solmaz, Ali Riza Yildiz, Ferruh Ozturk
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2021)
Article
Mathematical & Computational Biology
Yodsadej Kanokmedhakul, Natee Panagant, Sujin Bureerat, Nantiwat Pholdee, Ali R. Yildiz
Summary: This study introduces a self-adaptive teaching-learning-based optimization algorithm for aircraft parameter estimation, which is shown to have good search performance through numerical validation and experimental results, making it a baseline for aircraft parameter estimation.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Betul Sultan Yildiz, Sumit Kumar, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, Ali Riza Yildiz
Summary: This paper proposes a new metaheuristic algorithm called Chaotic Levy flight distribution (CLFD) algorithm, which is aimed at solving engineering optimization problems in the physical world. The results of the study show that CLFD algorithm has advantages in solving optimization problems and can effectively find optimal solutions.
Article
Materials Science, Characterization & Testing
Ahmet Can Gunaydin, Ali Riza Yildiz, Necmettin Kaya
Summary: In this study, a multi-objective optimization is conducted using non-dominated sorting genetic algorithm-II to optimize the build orientation for three-dimensional parts in additive manufacturing. Estimation methods are developed to compute the amount of support structure and build time, and the optimized results are visualized and evaluated for complex parts. The automation of the preprocessing stage eliminates the need for design knowledge, contributing to the reduction of support structure volume and build time in additive manufacturing.
Article
Materials Science, Characterization & Testing
Betul Sultan Yildiz, Pranav Mehta, Sadiq M. Sait, Natee Panagant, Sumit Kumar, Ali Riza Yildiz
Summary: The article introduces a novel hybrid metaheuristic algorithm, HAHA-SA, based on the artificial hummingbird algorithm and simulated annealing problem, which shows dominance in efficiently solving complex multi-constrained design optimization problems.
Article
Engineering, Multidisciplinary
Bansi D. Raja, Vivek K. Patel, Ali Riza Yildiz, Prakash Kotecha
Summary: This paper compares the performance of scientific law-inspired optimization algorithms for real-life constrained optimization applications. The algorithms are evaluated using a constrained engineering application of the Stirling heat engine system. The effects of constraint handling methods and output constraints on algorithm performance are analyzed and presented.
ENGINEERING OPTIMIZATION
(2023)
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Betul Sultan Yildiz, Pranav Mehta, Natee Panagant, Sadiq M. Sait, Seyedali Mirjalili, Ali Riza Yildiz
Summary: A novel metaheuristic algorithm called Chaotic Marine Predators Algorithm (CMPA) is proposed for engineering problem optimization, which integrates the exploration merits of MPA and the exploitation capabilities of chaotic maps. The proposed algorithm is applied to decode complex design and manufacturing problems and its performance is evaluated on CEC 2020 numerical problems and constrained design problems. Additionally, case studies and statistical analysis are conducted to compare CMPA with other algorithms, showing its significantly improved performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
