Article
Automation & Control Systems
Kai Zhang, Gary G. Yen, Zhenan He
Summary: In this article, a recursive evolutionary algorithm EvoKnee(R) is proposed to directly search for global knee solutions and multiple local knee solutions using the minimum Manhattan distance approach, instead of a large number of Pareto optimal solutions. Unlike traditional approaches, only nondominated solutions in rank one are preserved in each generation, reducing computational cost and allowing quick convergence to knee solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Mathematics
Dongmei Jing, Mohsen Imeni, Seyyed Ahmad Edalatpanah, Alhanouf Alburaikan, Hamiden Abd El-Wahed Khalifa
Summary: This study conducted comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. Data were collected from the financial statements of companies listed on the Tehran Stock Exchange in 2020. After simulating the data and programming them with MATLAB software, the cumulative data analysis model was performed, and 24 companies were selected. The research findings showed that different multi-index decision-making methods (TOPSIS method), the taxonomy method (Taxonomy), ARAS method, VIKOR method, The COPRAS method, and the WASPAS method can all identify the optimal stock portfolio and the best stock portfolio for the highest return.
Article
Computer Science, Artificial Intelligence
Ke Li, Haifeng Nie, Huiru Gao, Xin Yao
Summary: This article presents a simple and effective knee point identification method that is attractive to decision makers in multicriterion decision making. The method validates whether a solution is a knee point by comparing its localized tradeoff utility with others within its neighborhood, and a solution is considered a knee point if it has the best-localized tradeoff utility among its neighbors. The GPU implementation reduces the worst-case complexity and the empirical results demonstrate the outstanding performance of the proposed method, especially on problems with many local knee points. The usefulness of the method in guiding evolutionary multiobjective optimization algorithms is also validated.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Review
Business
Ahmet Selcuk Yalcin, Huseyin Selcuk Kilic, Dursun Delen
Summary: Business analytics systems are significant investments for enterprises, improving performance and decision-making processes. Multi-criteria decision-making methods play an important role in BA practices, making the concepts of business analytics and decision-making inseparable. This review provides a comprehensive examination of the use of MCDM methods in BA.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Computer Science, Information Systems
Shyi-Ming Chen, Guan-Lin Lu
Summary: This paper proposes a new multiple attribute decision making (MADM) method based on a nonlinear programming (NLP) model, which utilizes a constructed score matrix (SCMX) and a score function (SF) of interval-valued intuitionistic fuzzy values (IVIFVs) to obtain optimal weights (OWs) for attributes. The proposed method overcomes the shortcomings of existing MADM methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Binyu Luo, Yicheng Ye, Nan Yao, Qihu Wang
Summary: This study mined information of interval numbers, including non-positive ones, in a rectangular coordinate system to define a ranking method and validate its feasibility and effectiveness through examples. The method can rank interval numbers intuitively and represent decision-makers’ multiple attitudes with different risk appetites.
Article
Mathematics, Applied
Aliya Fahmi, Fazli Amin, Sayed M. Eldin, Meshal Shutaywi, Wejdan Deebani, Saleh Al Sulaie
Summary: Multiple attribute decision-making is significant in our everyday life. Fuzzy model and its extensions are widely used to solve the problems of decision makers feeling uncertain in choosing suitable assessment values. In this study, we proposed an innovative Schweizer-Sklar t-norm and t-conorm operation, Fermatean fuzzy Schweizer-Sklar operators, as a framework for an MCDM method. Through an example, we demonstrated its effectiveness and applicability. Furthermore, a comprehensive limitation study, rational examination, and comparative analysis showed that our technique provides decision makers with better choices and reduces restrictions on individual preferences.
Article
Computer Science, Artificial Intelligence
Wen Jiang, Meijuan Wang, Xinyang Deng
Summary: The study introduces a MADM method based on SVNS and preference relation, which takes into account the preference information between alternatives. By evaluating decision matrix and neutrosophic preference relation, the proposed method effectively determines the most desirable alternative according to decision makers' cognitive information.
COGNITIVE COMPUTATION
(2021)
Article
Multidisciplinary Sciences
Mehdi Keshavarz-Ghorabaee, Maghsoud Amiri, Edmundas Kazimieras Zavadskas, Zenonas Turskis, Jurgita Antucheviciene
Summary: Researchers introduced a new method called MEREC for determining the objective weights of criteria in multi-criteria decision-making problems. The method utilizes a novel idea for weighting criteria and computational analyses confirmed its efficiency and stability. Comparisons with other objective weighting methods showed the reliability of MEREC in determining criteria weights.
Article
Computer Science, Artificial Intelligence
Fernando Sitorus, Pablo R. Brito-Parada
Summary: This paper presents a hybrid Multiple Criteria Decision Making (MCDM) method that considers both qualitative and quantitative data in a probabilistic environment for group decision making. The method fuzzifies qualitative data and combines it with quantitative data to develop a hybrid model, allowing decision makers to adjust the weight of each quantitative model. An example is provided to demonstrate the application of the method for ranking and evaluating renewable energy technologies in the mining industry.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Social Sciences, Interdisciplinary
Bo-Wei Zhu, Ying He Xiao, Wei-Quan Zheng, Lei Xiong, Xia Yun He, Jian-Yi Zheng, Yen-Ching Chuang
Summary: This study evaluated the aesthetic expression of environmental design schemes in China and proposed a hybrid decision analysis model. By constructing a framework consisting of 5 dimensions and 18 evaluation elements, the key design elements and their influence relationships were identified.
Article
Computer Science, Information Systems
Xin-Yao Zou, Shyi-Ming Chen, Kang-Yun Fan
Summary: A new MADM method is proposed in this paper using probability density functions and TDMx in IVIF environments. This method has significant advantages in computational efficiency and result accuracy.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Shyi-Ming Chen, Heng-Li Deng
Summary: In this paper, a novel multiattribute decision making method is proposed using nonlinear programming and a score function based on interval-valued intuitionistic fuzzy values. The method overcomes the drawbacks of existing methods and offers a useful approach for decision making in interval-valued intuitionistic fuzzy settings.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Amirhossein Najafi, Alireza Nemati, Mahdi Ashrafzadeh, Sarfaraz Hashemkhani Zolfani
Summary: Cardiovascular diseases are deadly illnesses that affect many people worldwide. Diagnosing heart disease on time plays a significant role in healthcare as it reduces the chances of death and saves costs. This article presents an efficient system using artificial neural networks, feature selection methods, and multiple-criteria decision-making techniques for heart disease diagnosis.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Bin Xu, Yu Sun, Xin Huang, Ping-an Zhong, Feilin Zhu, Jianyun Zhang, Xiaojun Wang, Guoqing Wang, Yufei Ma, Qingwen Lu, Han Wang, Le Guo
Summary: This study proposes a multiobjective robust optimization and decision-making framework to minimize the multiple risks of a cascade hydropower system through robust operation. The framework includes risk analysis, robust control, and decision-making models. The findings suggest that robust optimization can effectively reduce the risk values of ecological water shortfall, consumptive water shortfall, and energy shortfall compared to chance-constrained programming, but it also leads to tradeoffs in energy production efficiency and water usage efficiency.
WATER RESOURCES RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Yuqiao Liu, Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen, Kay Chen Tan
Summary: Deep neural networks have achieved great success in many applications, but their architectures require labor-intensive and expert-designed processes. Neural architecture search (NAS) technology enables automatic design of architectures, with evolutionary computation (EC) methods gaining attention and success. However, there is currently no comprehensive summary of EC-based NAS algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Zhichao Sun, Hang Ren, Huarui Sun, Gary G. Yen, Junjie Wu, Jianyu Yang
Summary: This article investigates the terminal trajectory planning for synthetic aperture radar (SAR) imaging guidance. A chronological iterative search framework (CISF) is proposed to solve the trajectory planning problem by decomposing it into subproblems and utilizing the optimization results of preceding subproblems. Experimental studies show the effectiveness and superiority of CISF compared to other methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhichao Sun, Gary G. Yen, Junjie Wu, Hang Ren, Hongyang An, Jianyu Yang
Summary: This article proposes a mission planning framework for an energy-efficient passive UAV radar imaging system and introduces a path planning method called Sub-DiCoS. The method adjusts the UAV's flight path and utilizes differential evolution and the whole-stage best guidance technique to achieve optimized imaging and communication performance in an energy-efficient manner.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Chien-Feng Wu, Chi-Kwang Hwang, Wei-Yu Chiu
Summary: This paper proposes a method for solving observer-based multi-objective optimal control problems (MOCP) using a Takagi-Sugeno (T-S) fuzzy model to approximate nonlinear stochastic jump-diffusion systems (NSJDS). The problem is transformed into an MOCP with linear matrix inequality (LMI) constraints, and a grid-based front-squeezing searching algorithm (GBFSA) is used to efficiently search for the Pareto front while an approach based on minimum Manhattan distance (MMD) is used to select a preferred Pareto controller.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Weifeng Gao, Zhifang Wei, Maoguo Gong, Gary G. Yen
Summary: This article proposes a decomposition differential evolution algorithm based on radial basis function to solve multimodal optimization problems. The algorithm decomposes the problem into multiple global optimization subproblems and solves them using population update strategy and local RBF surrogate models.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhichao Sun, Hang Ren, Gary G. Yen, Tianfu Chen, Junjie Wu, Hongyang An, Jianyu Yang
Summary: In this article, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm (CRS-DE) is proposed to solve Highly Constrained Multiobjective Optimization Problems (HCMOPs). The algorithm relaxes the constraints by dividing the infeasible solutions into semifeasible subpopulation (SF) and infeasible subpopulation (IF), and devises corresponding reproduction and selection strategies for SF, IF, and feasible subpopulations. To prevent premature convergence, a mobility restriction mechanism is developed to restrict the individuals in SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Electrical & Electronic
Zhichao Sun, Junjie Wu, Gary G. Yen, Zheng Lu, Jianyu Yang
Summary: This paper investigates the performance and implementation of an energy-efficient passive UAV radar imaging system. Equipped with a synthetic aperture radar (SAR) receiver, the system passively reuses the backscattered signal of an external illuminator, achieving SAR imaging and data communication. The article presents the system concept, analyzes the imaging performance and feasibility for typical illuminators, and establishes a set of mission performance evaluators for comprehensive assessment.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Hong Li, Weifeng Gao, Jin Xie, Gary G. Yen
Summary: This article presents a method for automatically designing the network architecture of multilayer perceptron (MLP) neural networks and optimizing network parameters using a multiobjective bilevel programming model. The upper level constructs a multiobjective optimization problem to obtain a set of Pareto optimal network structures for the MLPs, while the lower level solves a single-objective optimization problem to search for the optimum network parameters. A novel multiobjective hierarchical learning algorithm (MOHLA) is proposed to efficiently deal with this model, and a selective ensemble strategy is adopted to improve identification accuracy. Experimental results confirm the excellent performance of MOHLA.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Siyi Li, Yanan Sun, Gary G. Yen, Mengjie Zhang
Summary: With the rise of smart electronics and mobile devices, existing high-accuracy CNN models are difficult to apply due to limited resources. In this article, we propose an automatic method for designing CNN architectures under constraint handling, which effectively searches for optimal network models meeting preset constraints.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yingbai Hu, Zhijun Li, Gary G. Yen
Summary: This article proposes an autonomous motion planning method at the torque level for high-speed manipulation robots, considering multiple conflicting performance metrics. The method can surpass motion limits set by traditional approaches, with low energy consumption and high precision.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zequn Sun, Wei Hu, Chengming Wang, Yuxin Wang, Yuzhong Qu
Summary: Entity alignment, the discovery of identical entities across different knowledge graphs, is a critical task in data fusion. Existing entity alignment methods lack robustness to long-tail entities and the absence of entity names or relation triples. This paper proposes a robust and adaptive entity alignment method that does not require relations, attributes, or names, achieving state-of-the-art performance even in challenging settings without relations and names.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Zaixing Sun, Boyu Zhang, Chonglin Gu, Ruitao Xie, Bin Qian, Hejiao Huang
Summary: In this article, a hybrid heuristic algorithm called ET2FA is proposed to solve deadline-constrained workflow scheduling in the cloud. With new features such as hibernation and per-second billing, ET2FA can generate efficient and economical scheduling schemes. Extensive simulation experiments show that ET2FA outperforms state-of-the-art algorithms.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Qiyu Sun, Gary G. G. Yen, Yang Tang, Chaoqiang Zhao
Summary: Monocular depth estimation, achieved through deep learning, is a fundamental task in environmental perception. However, trained models often exhibit degraded performance when applied to new datasets due to dataset differences. We propose a meta-learning framework with an adversarial depth estimation task to improve the transferability and alleviate meta-overfitting issues of self-supervised monocular depth estimation models. Our method demonstrates fast adaptation to new domains and achieves comparable results to state-of-the-art methods after only 0.5 epoch of training.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junhao Huang, Bing Xue, Yanan Sun, Mengjie Zhang, Gary G. Yen
Summary: Neural architecture search (NAS) is a popular research topic in deep learning community due to its potential in automating the construction of deep models. Among various NAS approaches, evolutionary computation (EC) stands out for its capability of gradient-free search. However, most current EC-based NAS approaches have the limitation of discrete evolution, making it difficult to handle the number of filters for each layer flexibly. Additionally, EC-based NAS methods are criticized for their inefficiency in performance evaluation, often requiring full training of hundreds of candidate architectures. This work proposes a split-level particle swarm optimization (PSO) approach to address these issues and achieves superior performance on image classification benchmarks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Haokai Hong, Min Jiang, Gary G. Yen
Summary: The large-scale multiobjective optimization problem (LSMOP) involves optimizing multiple conflicting objectives and hundreds of decision variables. Existing algorithms often focus on improving performance but pay little attention to improving insensitivity. We propose an evolutionary algorithm based on Monte Carlo tree search to improve the performance and insensitivity of solving LSMOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)