Article
Computer Science, Artificial Intelligence
Fangqing Gu, Haosen Liu, Yiu-ming Cheung, Hai -Lin Liu
Summary: This study proposes an adaptive constraint regulation method to balance the feasibility and convergence of solutions by adjusting the constraint violation of infeasible solutions. Experimental results demonstrate that the proposed method effectively achieves solution balance and improves solution diversity.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mu-En Wu, Jia-Hao Syu, Jerry Chun-Wei Lin, Jan-Ming Ho
Summary: The study introduces an evolutionary ORB-based model to optimize thresholds and develop protective closing strategies for enhanced profitability. Through evolutionary computation, rational strategies and parameters were derived, leading to improvements in annual returns by 2.8% and Sharpe ratio by 1.0, while reducing maximum drawdown by half.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Yingbo Xie, Junfei Qiao, Ding Wang, Baocai Yin
Summary: The paper proposes a novel multiobjective optimization evolutionary algorithm, MOEA/D-IMA, based on improved adaptive dynamic selection strategies and elite archive strategy to enhance population diversity and convergence; experimental results show that MOEA/D-IMA significantly improves optimization performance when dealing with MOPs.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Juan Long, Jingshan Liu, Jin Mei
Summary: The proposed method, RAMEA, combines Gaussian sampling and neighbor based mating to generate offspring solutions and has shown advantages over other algorithms in several test instances with complicated characteristics.
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
Computer Science, Artificial Intelligence
Yuxuan Luan, Junjiang He, Jingmin Yang, Xiaolong Lan, Geying Yang
Summary: This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address the common challenge faced by many existing algorithms in solving real-world optimization problems. By employing strategies such as uniform initialization and self-organizing map, the algorithm improves the population diversity and uniformity. Comparative analysis with 13 other algorithms validates the superiority of the proposed algorithm in terms of uniformity and objective function balance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Qinqin Fan, Yilian Zhang, Ning Li
Summary: The paper introduces an automatic selection strategy of multiobjective evolutionary algorithms based on performance indicators (MOEAS-PI). This strategy can effectively improve the efficiency and robustness of solving multiobjective optimization problems.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Wu Lin, Zexuan Zhu, Maoguo Gong, Jianqiang Li, Carlos A. Coello Coello
Summary: This article proposes a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces to maintain diversity in solutions. Experimental results validate the advantages of this approach in maintaining diversity in both objective and decision spaces.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Zhengping Liang, Tiancheng Wu, Xiaoliang Ma, Zexuan Zhu, Shengxiang Yang
Summary: In recent years, dynamic multiobjective optimization problems (DMOPs) have gained increasing attention. This article proposes a dynamic multiobjective evolutionary algorithm (DMOEA-DVC) based on decision variable classification, aiming to balance population diversity and convergence. Experimental results comparing DMOEA-DVC with six other algorithms on 33 benchmark DMOPs demonstrate its superior overall performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, Carlos A. Coello Coello
Summary: This paper proposes the definition of the biparty multiobjective optimal power flow (BPMOOPF) problem and introduces a novel evolutionary biparty multiobjective optimization algorithm (BPMOOPF-EA) to solve the problem. Experimental results show that BPMOOPF-EA outperforms other algorithms in solving the MOOPF problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Naili Luo, Yulong Ye, Wu Lin, Qiuzhen Lin, Victor C. M. Leung
Summary: A novel multimodal multiobjective memetic algorithm is proposed in this paper, which preserves more global and local Pareto optimal solution sets using a local detection mechanism and a clustering-based selection strategy. Experimental results demonstrate the superior performance of the proposed algorithm.
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Kunjie Yu, Boyang Qu, Jing Liang, Caitong Yue, Xuanxuan Ban
Summary: In this article, an evolution-based constrained multiobjective feature extraction method (ECMOFE) is proposed, which leverages the information generated in the evolutionary process to form the feature matrix. Two populations are created to optimize constraints and objectives, and two complementary evolutionary operators are used to generate offspring for each population. The successful rate of offspring individuals generated by each operator of each population is recorded to form the feature matrix. Based on the formed features, several algorithm recommendation methods are built on the basis of classifiers. The results based on multiple metrics show the effectiveness of the proposed ECMOFE.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Ray Lim, Abhishek Gupta, Yew-Soon Ong, Liang Feng, Allan N. Zhang
Summary: This paper presents a new perspective on domain adaptation in evolutionary optimization, inducing positive transfers even in scenarios of source-target domain mismatch. By establishing a probabilistic formulation and proposing a domain adaptive transfer evolutionary algorithm, it is significant for solving complex problems.
COGNITIVE COMPUTATION
(2021)
Article
Optics
Rafael G. Gonzalez-Acuna, Jeck Borne, Simon Thibault
Summary: This study investigates the nonparaxial diffraction of the Cartesian oval in finite and infinite conjugate configurations. The refraction and pupil apodization function of the Cartesian oval are obtained through analytic equations and compared with other optical elements. The findings suggest that a Cartesian oval is not suitable for tight focusing and super-resolution applications for distant objects, but performs similarly to an aplanatic lens in a finite conjugate configuration, and sometimes even better.
OPTICAL ENGINEERING
(2022)
Article
Optics
Tristan Chabot, Denis Brousseau, Hugues Auger, Simon Thibault
Summary: We proposed a model to estimate the surface topography and transition edge width in diamond-turned non-circular compound freeform optics. The model takes into consideration various factors such as cutting and tooling parameters, material response and defects, tool wear, and spindle vibrations. Fabrication tests have shown good agreement between the model and experimental results, providing valuable insights for optical engineers in choosing diamond tips and assessing surface quality in compound freeform designs.
OPTICAL ENGINEERING
(2022)
Article
Optics
Charles Pichette, Michel Piche, Pierre Marquet, Simon Thibault
Summary: In recent years, the GRENOUILLE technique has been used to fully characterize the electric field of ultrashort laser pulses in a single shot. However, aberrations can distort the measurement and lead to spectral or temporal profile distortions as well as reduced second-harmonic signal intensity. Some distortions may be mistakenly attributed to pulse-front tilt or spatial chirp when they are actually caused by aberrations, complicating the identification of the real source of the distortions.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2022)
Article
Optics
Rafael G. Gonzalez-Acuna, Simon Thibault
Summary: We present a new formalism based on Richards-Wolf theory for rigorously modeling the nonparaxial focusing of radially polarized electromagnetic beams with freeform wavefronts. The approach is validated by comparing with known results obtained by Richards-Wolf theory and is thoroughly discussed and solved for various freeform wavefronts that have not been analytically treated before. The extension of the method to other polarization states is straightforward.
Article
Optics
Alexandre Cleroux Cuillerier, Jeck Borne, Simon Thibault
Summary: We propose a new method that integrates metasurfaces in optical design using semi-analytical modeling of dielectric nanostructures. This method calculates the output phase of an electric field incident on the metasurface, allowing it to be used with ray-tracing software. By avoiding time-consuming computation, this tool offers a way to use metasurfaces in optical systems and incorporates built-in optimization processes. Our approach demonstrates the applicability and versatility of our method through variations of a triplet system composed of refractive elements and a metasurface, achieving similar optical performances. By leveraging the richness of metasurfaces and conventional lens design software, our unique and innovative method of combining metasurfaces and ray-tracing has the potential to promote the design of innovative systems.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
(2023)
Article
Optics
Tristan Chabot, Denis Brousseau, Simon Thibault
Summary: In this paper, we discuss the process of stray light analysis in optical systems with image slicers. We provide guidelines on the use of scattering models depending on the fabrication of the image slicer assembly. Our work covers the determination of ray paths, computing cross-talk on pupil mirrors due to scattering, quantifying ghost image intensity, and determining baffle positions. We also consider diffraction caused by narrow slice apertures of the image slicer, which contributes to unwanted light through cross-talk on pupil mirrors. Our work serves as a valuable resource for optical engineers working on image slicer-based systems, offering a clear and comprehensive procedure to obtain accurate estimates.
OPTICAL ENGINEERING
(2023)
Article
Optics
Bahram Javidi, Hong Hua, Adrian Stern, Manuel Martinez-Corral, Osamu Matoba, Ana Doblas, Simon Thibault
Summary: This Feature Issue of Optics Express is a collection of 31 articles presented at the 2022 Optica conference on 3D Image Acquisition, Display: Technology, Perception and Applications. The articles cover the topics and scope of the conference. This Introduction provides a summary of the published articles in this Feature Issue.
Article
Optics
Loic Tabourin, Denis Brousseau, Simon Thibault, Tigran Galstian
Summary: This study investigates the capability of an electrically tunable liquid crystal lens (TLCL) to dynamically generate various wavefront shapes. The influence functions and crosstalk between them are characterized by adjusting voltage and frequency of electrical signals. The obtained results are important for designing adaptive optical systems that require dynamic wavefront control.
Proceedings Paper
Optics
J. Buquet, S-G Beauvais, J. Parent, P. Roulet, S. Thibault
Summary: This paper presents experimental results from a new wide-angle vision camera module optimized for low-light conditions. The results include different sense and avoid functionalities as well as preliminary results using the camera module images on neural networks for scene understanding tasks.
EMERGING IMAGING AND SENSING TECHNOLOGIES FOR SECURITY AND DEFENCE VII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Zhenfeng Zhuang, Jocelyn Parent, Patrice Roulet, Simon Thibault
Summary: This paper proposes a compact design scheme for wide-angle lenses that addresses the distortion issue using freeform surfaces. The design approaches, including starting point selection, freeform surface conversion scheme, and system optimization, are discussed in detail. Additionally, diagnostic tools for freeform surfaces are developed to facilitate system optimization and visualize crucial aspects of optical performance. Simulation results demonstrate the effectiveness of the proposed design strategies in reducing distortion and improving optical performance.
CURRENT DEVELOPMENTS IN LENS DESIGN AND OPTICAL ENGINEERING XXIII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Guillaume Allain, Simon Thibault
Summary: Bio-inspired optical design draws inspiration from natural selection to introduce non-conventional techniques or material for better performance or novel applications. This paper presents the design of an optical system that combines the form-factor and optical properties of the human eye, with a focus on using distortion as a solution for novel problems.
CURRENT DEVELOPMENTS IN LENS DESIGN AND OPTICAL ENGINEERING XXIII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
J. Buquet, R. Larouche, J. Parent, P. Roulet, S. Thibault
Summary: The optical design process aims to minimize aberrations by using optimization methods and relies on key performance indicators (KPIs) to evaluate system performance. We developed an optical and imaging simulation pipeline that accurately simulates the effects of complex optical designs and image sensors, allowing us to study the impact of additional aberrations.
CURRENT DEVELOPMENTS IN LENS DESIGN AND OPTICAL ENGINEERING XXIII
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Julie Buquet, Jocelyn Parent, Jean-Francois Lalonde, Simon Thibault
Summary: Data driven approaches are efficient in many vision tasks and are now being used for optical parameters optimization. The complexity and entanglement of optical parameters in wide-angle systems present new challenges, which are investigated through the establishment of a data-driven prediction model.
CURRENT DEVELOPMENTS IN LENS DESIGN AND OPTICAL ENGINEERING XXIII
(2022)
Proceedings Paper
Instruments & Instrumentation
N. Blind, U. Conod, A. de Meideros, F. Wildi, F. Bouchy, S. Bovay, D. Brousseau, A. Cabral, L. Genolet, J. Kolb, R. Schnell, A. Segovia, M. Sordet, S. Thibault, B. Wehbe, G. Zins
Summary: This paper presents the design and performance of the NIRPS Front-End, as well as lessons learned along the way.
GROUND-BASED AND AIRBORNE INSTRUMENTATION FOR ASTRONOMY IX
(2022)
Proceedings Paper
Engineering, Mechanical
Tristan Chabot, Jeck Borne, Guillaume Bedard, Simon Thibault
Summary: This paper presents a conceptual design of a metasurface-based image slicer, which can control the position and aberrations of the slices by adjusting the surface phases. The design achieves good spectral resolution and transmission in the J band.
ADVANCES IN OPTICAL AND MECHANICAL TECHNOLOGIES FOR TELESCOPES AND INSTRUMENTATION V
(2022)
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)
