Article
Computer Science, Artificial Intelligence
Jesus Guillermo Falcon-Cardona, Raquel Hernandez Gomez, Carlos A. Coello Coello, Ma. Guadalupe Castillo Tapia
Summary: This paper presents a survey of parallel implementations of multi-objective evolutionary algorithms (pMOEAs), discussing their significance in tackling computationally expensive applications, describing taxonomy and methods review, and proposing open questions for further research.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Pei-Qiu Huang, Xiangsong Kong, Jing Zhao
Summary: This paper proposes a novel constrained multi-objective evolutionary algorithm called CMAOO, which optimizes an (M+1)-objective optimization problem consisting of the original M objective functions and the degree of constraint violation. It constructs a main population and saves all feasible solutions in an external archive. The main population and the external archive are evolved to search the whole space and the feasible regions, respectively, and their offspring update the external archive and the main population separately. Experimental studies show that CMAOO is competitive in solving constrained multi-objective optimization problems compared to four state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yanping Wang, Yuan Liu, Juan Zou, Jinhua Zheng, Shengxiang Yang
Summary: Balancing convergence and diversity in constrained multi-objective optimization problems is challenging. Existing evolutionary algorithms are insufficient, hence a novel algorithm named DTAEA is proposed. DTAEA divides the population's evolutionary process into two phases to improve exploration capability and guide population distribution in feasible regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Yi Xiang, Jinhua Zheng, Yaru Hu, Yuan Liu, Juan Zou, Qi Deng, Shengxiang Yang
Summary: This paper proposes a multimodal multi-objective algorithm based on weak relationship indicators, which allows the population to retain solutions from different Pareto sets during exploration. An archive based on weak convergence indicators is also introduced to retain excellent solutions.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Ihsan Elahi, Hamid Ali, Muhammad Asif, Kashif Iqbal, Yazeed Ghadi, Eatedal Alabdulkreem
Summary: This article proposes an improved multi-objective group counseling optimizer algorithm, which shows better performance in solving problems compared to other algorithms. Additionally, applying this algorithm for optimization scheduling can significantly reduce freshwater consumption in the textile dyeing industry.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xiangsong Kong, Yongkuan Yang, Zhisheng Lv, Jing Zhao, Rong Fu
Summary: This paper proposes a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to address the issue of balancing feasibility, convergence, and diversity in constrained multi-objective optimization problems. DDCMEA employs a dynamic dual-population co-evolution strategy to balance convergence and feasibility by adjusting the offspring number of the two populations. In the early stage, the algorithm focuses on convergence and generates more offspring of the first population, while in the late stage, it focuses on feasibility and generates more offspring of the second population. The results show that DDCMEA achieves competitive performance in handling constrained multi-objective optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohamed Abouhawwash, Adam M. Alessio
Summary: This study proposes a multi-objective optimization algorithm for PET image reconstruction using a genetic algorithm to generate solutions optimal for multiple tasks. The method demonstrates improved objective function values compared to conventional approaches and identifies a diverse set of solutions in the multi-objective function space.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ye Tian, Jiaxing Hu, Cheng He, Haiping Ma, Limiao Zhang, Xingyi Zhang
Summary: In this paper, a novel surrogate-assisted evolutionary algorithm is proposed, which employs a surrogate model to conduct pairwise comparisons between candidate solutions instead of directly predicting solutions' fitness values. Compared to regression and classification models, the proposed model based on pairwise comparison can better balance between positive and negative samples, and be directly used, reversely used, or ignored based on its reliability in model management. The experimental results on abundant benchmark and real-world problems demonstrate that the proposed surrogate model is more accurate and outperforms state-of-the-art surrogate models.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Theory & Methods
Raquel Espinosa, Fernando Jimenez, Jose Palma
Summary: Air pollution forecasting modeling is crucial for improving air quality, ecosystems, and human health. This paper presents a novel spatio-temporal approach based on multi-objective evolutionary algorithms for air pollution forecasting. The proposed method, which identifies and combines multiple non-dominated linear regression models, has achieved promising results in predicting NO2 levels in southeastern Spain.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Theory & Methods
Raquel Espinosa, Fernando Jimenez, Jose Palma
Summary: This paper proposes a novel spatio-temporal approach based on multi-objective evolutionary algorithms for air pollution forecasting, showing promising results in the prediction of NO2 in southeastern Spain and opening up a new field in air pollution engineering.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jesus L. Llano Garcia, Raul Monroy, Victor Adrian Sosa Hernandez, Carlos A. Coello Coello
Summary: The paper presents an indicator-based evolutionary multi-objective optimization algorithm (EMOA) for tackling Equality Constrained MOPs (ECMOPs). By calculating the Inverted Generational Distance of a population and using it as a density estimator, the proposed COARSE-EMOA can properly approximate a feasible solution.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Adrian Florea, Ioana Cofaru, Andrei Patrausanu, Nicolae Cofaru, Ugo Fiore
Summary: The design of road car suspension models is crucial for both driver comfort and safety. As reported by the World Health Organization (WHO), road traffic injuries are a major cause of death globally, and it is predicted to become even more prevalent in the future. Road infrastructure, particularly with the rise of autonomous driving, is a key factor to consider in suspension model design. Furthermore, there is a growing need to study the impact of road conditions on autonomous vehicles. This work contributes by providing a platform for suspension system optimization, implementing various models and algorithms to achieve optimal configurations for different types of cars.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Shao, Jerry Chun-Wei Lin, Gautam Srivastava, Dongdong Guo, Hongchun Zhang, Hu Yi, Alireza Jolfaei
Summary: This article introduces a method for optimizing deep reinforcement learning models using neural evolutionary algorithms to solve combinatorial optimization problems. The proposed end-to-end multi-objective neural evolutionary algorithm demonstrates competitive and robust performance on the classic travel salesman problem and knapsack problem, and also performs well in inference time.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
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)