Article
Management
Marc Goerigk, Stefan Lendl, Lasse Wulf
Summary: This study focuses on two-stage robust optimization problems as games between a decision maker and an adversary. By adding an extra adversary stage, the study extends the problem into min-max-min-max problems, advancing from two-stage settings towards more general multi-stage problems. The study specifically examines budgeted uncertainty sets and explores both continuous and discrete cases.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Cuicui Yang, Tongxuan Wu, Junzhong Ji
Summary: This study proposes a multimodal multi-objective optimization evolutionary algorithm based on two-stage species conservation to solve MMOPs with local PSs. The algorithm divides the evolutionary process into diversity-oriented species conservation and convergence-oriented species conservation. Experimental results demonstrate the algorithm's ability to find global and local PSs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jing Liang, Leiyu Zhang, Kunjie Yu, Boyang Qu, Fuxing Shang, Kangjia Qiao
Summary: In this study, an interactive niching-based two-stage evolutionary algorithm for constrained multiobjective optimization (INCMO) is proposed. The algorithm uses two populations to optimize the multiobjective optimization problem and employs different niching mechanisms to maintain population diversity. The proposed method outperforms other methods in terms of performance on various test suites.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Management
Immanuel M. Bomze, Markus Gabl, Francesca Maggioni, Georg Ch. Pflug
Summary: This paper examines two-stage stochastic decision problems, specifically focusing on the quadratic objective function and the minimization of expected quadratic cost function within the standard simplex. The approach of finding bounds and solving simpler approximate problems yields satisfactory solutions. The paper also discusses potential applications and provides numerical results.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Rui Li, Yubo Tian, Pengfei Li
Summary: The proposed two-stage Gaussian process surrogate model considering sensitivity information can significantly reduce the demand for high-fidelity training samples during modeling, more accurately reflecting the mapping relationship between antenna input and output, and is suitable for problems with insufficient electromagnetic simulation samples.
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Haizhu Bao, Ling Wang, Jie Cao, Jianxin Tang, Jonrinaldi
Summary: This paper presents a multipopulation cooperative coevolutionary framework to improve the performance of the whale optimization algorithm (WOA). The framework utilizes a two-stage orthogonal learning mechanism and introduces a prediction model and an auxiliary vector pool to enhance the algorithm's exploration ability. It also divides the population into different subpopulations based on domain knowledge and enhances the cooperative coevolution among individuals. Experimental results demonstrate that the proposed algorithm outperforms 15 state-of-the-art algorithms in terms of efficiency and significance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Thermodynamics
Yudong Wang, Junjie Hu
Summary: This paper proposes a two-stage energy management method for integrated energy system (IES) considering the energy pre-transaction behavior of energy service provider (ESP) and users. The proposed method improves the profitability of ESP and users, promotes the utilization ratio of renewable energy, and reduces the carbon emissions of the system.
Article
Computer Science, Interdisciplinary Applications
Hai Tao, Nawfel M. H. Al-Aragi, Iman Ahmadianfar, Maryam H. Naser, Rania H. Shehab, Jasni Mohamad Zain, Bijay Halder, Zaher Mundher Yaseen
Summary: The resolution of optimization issues is a continuous topic of study and debate in academia. Biogeography-based optimization (BBO) is proposed as a population-based approach to solving complex optimization problems. However, the nature of its operators may cause it to become stuck in sub-optimal solutions, slowing down convergence and increasing computing time. To address these issues, a version of BBO called RBBO is proposed, incorporating a ranked-based strategy, an exponential dynamic Brownian random differential mechanism, and a successful adaptive random differential mutation mechanism. Experimental findings demonstrate that RBBO outperforms conventional BBO in terms of efficiency and accuracy, making it a promising technique for solving challenging tasks.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Yong Wang, Zhen Liu, Gai-Ge Wang
Summary: Recently, multimodal multi-objective problem (MMOP) has attracted significant attention in the field of multi-objective optimization problems. The proposed algorithm addresses the issue of finding all equivalent Pareto sets in MMOP by introducing a modified maximum extension distance (MMED) indicator and implementing two-stage and novel mutation strategies. Additionally, a MMED-based environmental selection strategy improves the overall performance of the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Mechanical
Canran Li, Nianfeng Wang, Kunjie Chen, Xianmin Zhang
Summary: This study introduces a workspace called prescribed flexible orientation workspace (PFOW) to characterize the orientation capability of PPMs. By building performance characteristic indices, the study helps optimize and compare 3-degree-of-freedom PPMs, and determines the best configuration through graphical performance comparison.
MECHANISM AND MACHINE THEORY
(2022)
Article
Management
Daniel Zhuoyu Long, Jin Qi, Aiqi Zhang
Summary: In this paper, we address a class of two-stage distributionally robust optimization problems with the property of supermodularity. We propose an efficient method that utilizes the worst case expectation and worst case distribution of supermodular functions to obtain exact optimal solutions for these problems. Additionally, we provide a necessary and sufficient condition to determine if a given two-stage optimization problem exhibits the property of supermodularity. We also investigate the optimality of segregated affine decision rules for problems with supermodularity. Several classic problems are solved using our framework, demonstrating its computational efficiency.
MANAGEMENT SCIENCE
(2023)
Article
Engineering, Mechanical
Mohammad Reza Haghjoo, Jungwon Yoon
Summary: The novel methodology focuses on optimizing the control effort of an equivalent shadow robot through a two-stage synthesis algorithm, which is successfully applied to the popular eightbar Jansen mechanism, with numerical examples provided to demonstrate efficacy.
MECHANISM AND MACHINE THEORY
(2022)
Article
Automation & Control Systems
Kuo-Hao Chang, Cheng-Chieh Huang
Summary: In manufacturing, the selection of machines and allocation of buffers are crucial for system performance. This study introduces an efficient algorithm, MSBA, that simultaneously solves the machine selection and buffer allocation problems in a production line environment, outperforming existing methods in terms of effectiveness and efficiency.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Operations Research & Management Science
Marc Goerigkl, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a class of combinatorial optimization problems where a feasible solution can be constructed in two stages, using the minmax regret criterion. The general properties of the problem are established, with specific results shown for the shortest path and selection problems.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Environmental Sciences
Liujia Xu, Dan Niu, Tianbao Zhang, Pengju Chen, Xunlai Chen, Yinghao Li
Summary: This paper presents a two-stage model for short-term rainfall prediction using radar echo maps. The proposed model achieves more accurate and detailed predictions compared to existing methods. Experimental results demonstrate its superiority in terms of image detail and precipitation accuracy indexes.
Article
Engineering, Industrial
Fuqing Zhao, Xiaotong Hu, Ling Wang, Tianpeng Xu, Ningning Zhu, Ningning Zhu
Summary: This paper proposes a reinforcement learning-driven brain storm optimisation (RLBSO) idea to solve the multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. Experimental results show that RLBSO outperforms the comparison algorithm in addressing the problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Xiaotong Hu, Ling Wang, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: This paper proposes a brain storm optimization algorithm with feature information knowledge and learning mechanism, which improves the searching ability through information interaction and search strategies. The experimental results demonstrate that the algorithm has significant performance in solving practical problems.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Huan Liu, Fuqing Zhao, Ling Wang, Jie Cao, Jianxin Tang, Jonrinaldi
Summary: This paper proposes an estimation of distribution algorithm with multiple intensification strategies (EDA-MIS) to solve a typical kind of hybrid flow-shop scheduling problem. Through the processes of selection, recombination, sampling, and local search, the algorithm provides good solutions in terms of solution quality and computational efficiency.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Tianpeng Xu, Fuqing Zhao, Jianxin Tang, Songlin Du, Jonrinaldi
Summary: The improved Monarch Butterfly Optimization algorithm (KDLMBO) enables self-learning and improves efficiency. It uses neighborhood information from candidate solutions as prior knowledge and utilizes learning migration and learning butterfly adjusting operators to achieve self-learning collective intelligence. Experimental results demonstrate the efficiency and significance of the KDLMBO algorithm.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Zhenyu Wang, Ling Wang, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: This paper presents the adoption of the artificial bee colony algorithm (ABC) to solve complex continuous real-valued optimization problems. The ABC algorithm is improved by incorporating the maximum entropic epistasis (MEE) and adaptive mutation methods to enhance its performance in terms of effectiveness and robustness.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Qiaoyun Wang, Ling Wang
Summary: This study proposes an inverse reinforcement learning based moth-flame optimization algorithm, called IRLMFO. The algorithm utilizes the Q-learning mechanism to choose the right strategy and enhances its exploitation capability through a competition mechanism. Experimental results demonstrate that the IRLMFO algorithm outperforms state-of-the-art algorithms in large-scale real-parameter optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zuohan Chen, Jie Cao, Fuqing Zhao, Jianlin Zhang
Summary: This paper proposes a co-evolutionary differential evolution (CDE) algorithm with a differential grouping (DG) mechanism to solve complex optimization problems with partially separable variables. The algorithm decomposes the problem into independent sub-problems and allocates search resources using polling, upper confidence bound (UCB), and random access. Success-history-based parameter adaptation for differential evolution (SHADE) is adopted as the search engine. Experimental results on CEC2017 demonstrate that CDE achieves competitive search performance compared to peer algorithms.
COGNITIVE COMPUTATION
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Shilu Di, Ling Wang
Summary: This study proposes a hyperheuristic algorithm based on Q-learning to solve the energy-efficient distributed blocking flow shop scheduling problem. By selecting a suitable low-level heuristic algorithm based on historical information feedback, considering both total tardiness and total energy consumption in the initialization method, and designing acceleration and deceleration operations to optimize scheduling performance. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of efficiency and significance in solving this problem.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Yuebao Liu, Ningning Zhu, Tianpeng Xu, Jonrinaldi
Summary: Selecting a suitable algorithm in the real world is a challenging problem. This study introduces a novel approach that integrates hyper-heuristic and Q-learning techniques to address the challenges. The experimental results demonstrate the effectiveness and efficiency of the proposed method in solving optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Ningning Zhu, Fuqing Zhao, Jie Cao, Jonrinaldi
Summary: Differential evolution (DE) and estimation of distribution algorithm (EDA) have complementary advantages in solving complex optimization problems. Designing appropriate strategies to balance exploration and exploitation is important for obtaining high-precision solutions. KCACIL is a knowledge-driven co-evolutionary algorithm that achieves comprehensive collaboration between algorithms, strategies, and individuals through a cross-regional interactive learning mechanism.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Tao Jiang, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: This article proposes a co-evolutionary migrating birds optimization algorithm based on online learning policy gradient (CMBO-PG) to solve complex continuous real-parameter optimization problems. The algorithm uses a Gaussian estimation of distribution algorithm (GEDA) to generate solutions for the leading flock, while a multi-strategy learning mechanism is used to generate neighborhood solutions for the following flock, promoting exploration capability. The co-evolution of the two flocks is achieved through information-sharing and destruction-construction operations to maintain a balance between exploration and exploitation.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Gang Zhou, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: In this paper, a knowledge-driven cooperative scatter search algorithm (KCSS) is proposed to address the distributed blocking flow shop scheduling problem (DBFSP) and minimize the makespan. The KCSS algorithm utilizes scatter search as the basic optimization framework, and combines neighborhood perturbation operator and Q-learning algorithm to enhance the search process. Experimental results demonstrate the robustness and effectiveness of the proposed algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Bo Zhu, Ling Wang
Summary: This article introduces an estimation of distribution algorithm-based hyper-heuristic to solve the distributed assembly mixed no-idle permutation flowshop scheduling problem. By utilizing simple heuristic rules as low-level operations, the algorithm controls the low-level heuristics sequence in the solution space. The experimental results show that the proposed algorithm is significantly superior to the competitors in solving the problem.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Gang Zhou, Ling Wang
Summary: The integration of reinforcement learning technology into meta-heuristic algorithms has attracted attention for solving complex combinatorial optimization problems. This paper proposes a cooperative scatter search algorithm with Q-learning mechanism (QCSS) for addressing the DPFSP-SDST problem. Effective heuristic algorithms are used to construct an initial population with high quality and diversity. Q-learning is combined with domain knowledge-guided perturbation operators to balance exploration and exploitation capabilities of the QCSS algorithm. Experimental results on benchmark set demonstrate the robustness and effectiveness of the QCSS algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Zesong Xu, Xiaotong Hu, Tianpeng Xu, Ningning Zhu, Jonrinaldi
Summary: With the development of the economy and technology, distributed manufacturing and green manufacturing are important in intelligent manufacturing. This paper proposes an energy-efficient distributed assembly no-wait flow shop scheduling problem (EEDANWFSP) and an improved iterative greedy (IIG) algorithm is proposed to solve it. Experimental results show the effectiveness of IIG in addressing energy-efficient EEDANWFSP.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
