Article
Engineering, Aerospace
Jiyao Zhang, Jinsheng Guo, Yuan Liu, Zhen Wang, Yufei Feng, Huayi Li
Summary: The traditional satellite component layout optimization problem focuses on satellites with fixed module size. However, in actual engineering practice, the size of microsatellites can be adjusted. This paper proposes a hybrid algorithm to solve the component assignment and layout optimization problem for multi-module microsatellites, considering variable module size.
Article
Computer Science, Artificial Intelligence
Mehrdad Amirghasemi
Summary: This paper presents a parallel evolutionary metaheuristic that is effective and versatile, outperforming existing methods on multiple benchmark instances.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Theory & Methods
Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
Summary: Evolutionary Computation approaches, inspired by nature, provide a reliable and effective way to address complex problems in real-world applications. They have been used to improve machine learning models and quality of results, contributing to addressing challenges in the field.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Theory & Methods
Ye Tian, Langchun Si, Xingyi Zhang, Ran Cheng, Cheng He, Kay Chen Tan, Yaochu Jin
Summary: This article provides a comprehensive survey of state-of-the-art MOEAs for solving large-scale multi-objective optimization problems, categorizing them into different types and discussing their strengths and weaknesses. It also reviews benchmark problems for performance assessment and important applications, while also addressing remaining challenges and future research directions in evolutionary large-scale multi-objective optimization.
ACM COMPUTING SURVEYS
(2021)
Article
Construction & Building Technology
Cemre Cubukcuoglu, Pirouz Nourian, M. Fatih Tasgetiren, I. Sevil Sariyildiz, Shervin Azadi
Summary: Hospital facilities are complex buildings with configuration problems that can lead to inefficient transportation processes. The Quadratic Assignment Problem (QAP) is a well-known problem in Operations Research, but rarely used in architectural design practice. This paper introduces a QAP formulation for space planning processes in hospital renovation, along with a heuristic solver developed for architects. The tool minimizes internal transportation processes, solving distance and flow issues between facilities.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jialiang Sun, Xianqi Chen, Jun Zhang, Wen Yao
Summary: This paper explores a multimodal optimization method for satellite layout optimization design and proposes an improved niching-based cross-entropy method. Through investigations on CEC2013 benchmarks and satellite layout optimization design problem, the effectiveness and feasibility of the proposed method are validated, showing superior performance compared to several state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Lucas R. C. de Farias, Aluizio F. R. Araujo
Summary: This paper introduces a MOEA/D-UR algorithm based on decomposition, which utilizes a metric to detect improvements and a procedure to increase diversity in the objective space. Experimental results suggest that MOEA/D-UR is more effective in handling real-world problems and multi-objective scenarios compared to other algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Hakan Ezgi Kiziloz, Ayca Deniz
Summary: In this study, a robust framework for feature selection is built leveraging the multi-core nature of a regular PC. Multiple execution settings are facilitated through the use of two multiobjective selection algorithms, four initial population generation methods, and five machine learning techniques. Extensive experiments on 11 UCI benchmark datasets show remarkable improvement in terms of maximum accuracy.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
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, Artificial Intelligence
Thomas H. W. Back, Anna V. Kononova, Bas van Stein, Hao Wang, Kirill A. Antonov, Roman T. Kalkreuth, Jacob de Nobel, Diederick Vermetten, Roy de Winter, Furong Ye
Summary: This article discusses some major developments in the field of evolutionary algorithms over the past 30 years, including covariance matrix adaptation evolution strategy, multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. The article emphasizes the need for fewer algorithms and proper benchmarking procedures to determine the usefulness of newly proposed algorithms.
EVOLUTIONARY COMPUTATION
(2023)
Article
Acoustics
Lin Zhang, Tao Zhang, Huajiang Ouyang, Tianyun Li, Mo You
Summary: Layout optimization of elastic supports is an effective approach to address the resonance problem of an industrial post-manufactured pipeline. A new measurement-based layout optimization method is proposed in this study to overcome the drawbacks of previous methods. The proposed method only requires a small number of experimental modal parameters and can accurately realize the target natural frequencies. Experimental evidence of the effectiveness of the proposed method is provided through the optimization of a real L-shaped pipeline system.
JOURNAL OF SOUND AND VIBRATION
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Uwe Aickelin, Hadi Akbarzadeh Khorshidi, Rong Qu, Hadi Charkhgard
Summary: This special issue focuses on the application of multiobjective evolutionary optimization in machine learning. Optimization plays a crucial role in many machine-learning techniques, and there is still potential to further utilize optimization in machine learning. Each machine-learning technique has hyperparameters that can be adjusted through evolutionary computation and optimization, considering multiple criteria such as bias, variance, complexity, and fairness in model selection. Multiobjective evolutionary optimization can help meet these criteria for optimizing machine-learning models. Although some existing approaches transform the problem into a single-objective optimization problem, multiobjective optimization models are more effective in contributing to multiple intended objectives or criteria.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Wolfgang Banzhaf
Summary: We propose a new surrogate-assisted evolutionary algorithm for expensive multiobjective optimization. The algorithm uses two classification-based surrogate models, addresses dominance prediction problem using deep learning techniques, and integrates the surrogate models with multiobjective evolutionary optimization using a two-stage preselection strategy. Experimental results show the superiority of the proposed algorithm compared with several representative surrogate-assisted algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Hui Zhang, Xiaojuan Zheng
Summary: This paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm (KAMSA) for optimizing makespan and cost of workflow execution in cloud platforms. It divides large-scale decision variables into groups using divide-and-conquer technology to improve evolutionary search efficiency. Comparison with five state-of-the-art competitors demonstrates KAMSA's advantages in 18 out of 20 test cases.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Eric O. Scott, Mark Coletti, Catherine D. Schuman, Bill Kay, Shruti R. Kulkarni, Maryam Parsa, Chathika Gunaratne, Kenneth A. De Jong
Summary: Asynchronous evolutionary algorithms are popular for solving computationally expensive search and optimization problems using many processors. The SWEET strategy improves the ability of slow-evaluating individuals with higher fitness to multiply in the population. It shows effectiveness in optimizing problems with positive correlation between solution quality and evaluation time.
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)