Review
Computer Science, Artificial Intelligence
Feng Qin, Azlan Mohd Zain, Kai-Qing Zhou
Summary: This article systematically reviews the harmony search (HS) algorithm and its variants from three aspects: describing the basic HS principle, discussing the impact of HS improvement on algorithm performance, and analyzing the characteristics and applications of HS variants. It is found that the improvement of HS mainly focuses on parameter enhancement and the integration with other metaheuristic algorithms, providing future directions for enhancing HS.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Shubham Gupta
Summary: The modified harmony search algorithm (MHSA) improves the efficiency and accuracy of the search process by utilizing valuable information stored in harmony memory and modifying the search strategy through new formulations. Experimental validation and comparativeperformance study show that MHSA outperforms conventional HSA and other metaheuristic algorithms in terms of search efficiency as a global optimizer.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Dongmei Liu, Haibin Ouyang, Steven Li, Chunliang Zhang, Zhi-Hui Zhan
Summary: This paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. By using parameter dynamic adjustment strategy, autonomous decision-making search strategy, and local competition mechanism, it effectively improves the performance of CNN and the efficiency of hyperparameter configuration. In addition, the feasibility of LACHS algorithm in configuring CNN hyperparameters is verified through experiments on Fashion-MNIST dataset, CIFAR10 dataset, and expression recognition.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Hwang Yi, Inhan Kim
Summary: This paper proposes a novel metaheuristic optimization technique DECS-TAPS for rapid and optimal digital prototyping of architectural forms. The findings show that DECS-TAPS outperforms other algorithms in multi-objective optimization and computational effectiveness, while also reducing parameter dependence and increasing robustness.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Environmental Sciences
Wen-jing Niu, Zhong-kai Feng, Zhi-qiang Jiang, Sen Wang, Shuai Liu, Wei Guo, Zhen-guo Song
Summary: An enhanced harmony search (EHS) method is developed to address the issues of standard harmony search, improving search ability and convergence rate. Applied to numerical optimization and multireservoir operation problems, EHS outperforms existing methods in various cases, showing better results.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Sharif Naser Makhadmeh, Mohammed El-Abd
Summary: This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to solve blocking flow-shop scheduling problem. The algorithm enhances its performance by diversifying the population using the island model and improving solution quality using neighboring heuristics. Experimental results demonstrate the efficiency and competitiveness of the proposed algorithm in solving instances from different datasets.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Xiangzhou Gao, Tingrui Liu, Liguo Tan, Shenmin Song
Summary: This paper proposes a multioperator search strategy for multiobjective evolutionary algorithms, which improves the search efficiency by adaptively learning the manifold structure of the Pareto optimal solution set and Pareto optimal front.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Mathematical & Computational Biology
Di-Wen Kang, Li-Ping Mo, Fang-Ling Wang, Yun Ou
Summary: The AHS-DE-OBL algorithm proposed in this study utilizes innovative strategies such as differential evolution, adaptive adjustment of search domain, and opposition-based learning to improve upon the limitations of the harmony search algorithm, resulting in better global search ability and faster convergence speed.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Noor Syahirah Nordin, Mohd Arfian Ismail
Summary: This paper investigates the optimization problem of fuzzy systems and fuzzy modeling, and improves the Butterfly Optimization Algorithm by combining it with Harmony Search. The proposed method provides a way to achieve optimal results in fuzzy modeling. Experimental results show that the method achieves very high accuracy on two datasets.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Geon Hee Lee, Ali Sadollah, Sang Ho Park, Zong Woo Geem
Summary: There has been a development of many metaheuristic optimization algorithms inspired by various natural and artificial phenomena. While each algorithm should be coded for specific optimization problems, there is an increasing interest in developing software that can function generally and effectively. HS-Solver is an open-source software integrated with Microsoft Excel spreadsheet to solve various optimization problems using a music-inspired harmony search algorithm. This paper presents the basic structure of HS-Solver and its functionality in optimization examples.
Article
Automation & Control Systems
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Summary: This study aims to address multi-objective optimization problems with multiple black-box and heterogeneous objectives. It proposes a multi-objective Bayesian evolutionary optimization (BEO) approach that alleviates search biases and achieves a balance between convergence and diversity. The proposed algorithm is able to find high-quality solutions for heterogeneous multi-objective optimization problems compared with state-of-the-art methods.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhong-kai Feng, Wen-jing Niu, Shuai Liu
Summary: The CSA method, inspired by team cooperation behaviors, uses team communication, reflective learning, and internal competition operators to solve global optimization problems, demonstrating fast convergence and high search accuracy. It performs well in mathematical and engineering optimization problems, providing an effective tool for solving complex global optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Hongyou Cao, Yupeng Chen, Yunlai Zhou, Shuang Liu, Shiqiang Qin
Summary: This study investigates the performance of four improved penalty-free constraint-handling techniques (CHTs) in structural optimization and compares their capabilities. The mapping strategy shows superior search capability and stability, while the improved Deb rule is the most competitive in terms of computational efficiency.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Information Systems
Jing Jiang, Fei Han, Jie Wang, Qinghua Ling, Henry Han, Zizhu Fan
Summary: This paper proposes a novel decomposition-based MOEA that considers the ideal point as the global reference point and the nadir point as conditionally the local reference point to improve search diversity. The study shows that the nadir point may aid the ideal point in some cases and be redundant in others.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Kaiping Luo
COMPUTERS & INDUSTRIAL ENGINEERING
(2015)
Article
Operations Research & Management Science
Kaiping Luo, Xinhui Zhang
OPTIMIZATION LETTERS
(2015)
Article
Engineering, Industrial
Kaiping Luo
SYSTEMS ENGINEERING
(2015)
Article
Computer Science, Artificial Intelligence
Kaiping Luo
APPLIED SOFT COMPUTING
(2019)
Article
Engineering, Multidisciplinary
Kaiping Luo
ENGINEERING OPTIMIZATION
(2020)
Article
Computer Science, Artificial Intelligence
Kaiping Luo, Qiuhong Zhao
APPLIED SOFT COMPUTING
(2019)
Article
Engineering, Industrial
Kaiping Luo
Summary: Flexible process planning involves selecting and sequencing operations to minimize production cost or completion time. The proposed sequence learning harmony search algorithm intelligently finds proper immediate successors for each operation, outperforming other heuristics in solution quality and convergence rate.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Information Systems
Yuanrui Li, Qiuhong Zhao, Kaiping Luo
Summary: A novel multi-objective soft subspace clustering model is proposed in this study, which simultaneously optimizes three clustering criteria, measures the distance between data points in a composite kernel space, and optimizes the weight of base kernels using a multi-objective evolutionary algorithm to address the problems in conventional algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Meng Liu, Kaiping Luo, Junhuan Zhang, Shengli Chen
Summary: The study shows that the hybrid algorithm combining grey wolf optimizer and support vector machine can help achieve stable excess returns, improve the predictive performance of the support vector regression machine, and achieve better profitability and reliability in the Chinese A-share market.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Industrial
Kaiping Luo, Jianfei Sun, Liuwei Guo
Summary: This article focuses on the problem of flexible process planning and presents linear integer programming models to solve it. The proposed models have lower complexity and better performance compared to the latest mathematical programming models for process planning.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Kaiping Luo
Summary: The novel algorithm inspired by water flow in nature, Water Flow Optimizer (WFO), simulates hydraulic phenomena in optimizing global problems, showing competitive performance, good convergence, and parameter effects studied. Experimentally applied to solve spacecraft trajectory optimization problem with success.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Management
Kaiping Luo, Guangya Shen, Liheng Li, Jianfei Sun
Summary: Flexible Process Planning (FPP) is a key intelligent manufacturing technique that is formulated using 0-1 mathematical programming. The new formulation simultaneously considers alternative operation selection and sequencing and operational method assignment under two optimization criteria. The proposed linear models have lower complexity and better performance in solving benchmark instances compared to existing mathematical programming models.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Industrial
Guangya Shen, Kaiping Luo, Liheng Li
JOURNAL OF MANUFACTURING SYSTEMS
(2020)
Article
Computer Science, Interdisciplinary Applications
Kaiping Luo, Haihong Wang, Yijun Li, Qiang Li
COMPUTERS & OPERATIONS RESEARCH
(2017)
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)