Memetic Computing最新文献_第2页

Dynamic weighting label assignment for oriented object detection 面向对象检测的动态加权标签分配

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-08-02 DOI: 10.1007/s12293-024-00427-1

Yunhui Zhu, Buliao Huang

{"title":"Dynamic weighting label assignment for oriented object detection","authors":"Yunhui Zhu, Buliao Huang","doi":"10.1007/s12293-024-00427-1","DOIUrl":"https://doi.org/10.1007/s12293-024-00427-1","url":null,"abstract":"Oriented object detection has garnered significant attention for its broad applications in remote sensing image processing. Most oriented detectors perform dense predictions on a set of predefined anchors to generate oriented bounding boxes, where these anchors require classification (cls) and localization (loc) labels for detector training. Recent advancements in label assignment utilize the overall quality score of cls and loc predictions to determine positive and negative samples for each oriented object. However, these methods typically establish the overall quality score by assigning fixed weights to cls and loc quality scores. This approach may not be optimal, as fixed weights fail to dynamically balance cls and loc performance during model optimization, thereby constraining detection efficacy. Motivated by this observation, this paper proposes a Dynamic Weighting Label Assignment (DWLA) algorithm. DWLA dynamically adjusts the weights of individual quality scores based on the current model state to continuously balance cls and loc performance. Additionally, to mitigate the impact of unreliable predictions and achieve more stable training, this paper proposes a level-wise positive sample selection scheme and an object-adaptive scheme for constructing initial candidates of positive samples, respectively. Comprehensive experiments on the DOTA and UCAS-AOD datasets have validated the effectiveness of the proposed DWLA.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"36 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A memetic algorithm for improved joint route selection and split-level management in next-generation wireless communications 下一代无线通信中改进联合路由选择和分层管理的记忆算法

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-08-02 DOI: 10.1007/s12293-024-00418-2

Mario Garza-Fabre, Cristian C. Erazo-Agredo, Javier Rubio-Loyola

{"title":"A memetic algorithm for improved joint route selection and split-level management in next-generation wireless communications","authors":"Mario Garza-Fabre, Cristian C. Erazo-Agredo, Javier Rubio-Loyola","doi":"10.1007/s12293-024-00418-2","DOIUrl":"https://doi.org/10.1007/s12293-024-00418-2","url":null,"abstract":"The complexity of next-generation wireless communications, especially Beyond 5G and 6G communication systems, will be handled by artificial intelligence-based management paradigms. The joint selection of routes and functional split levels involves critical decisions that network infrastructure providers need to make to support requests from virtual Mobile Network Operators (vMNOs). These decisions comprise the assignment and configuration of physical network resources, which must comply with the specific quality of service restrictions of each vMNO request. Recent work defined a detailed mathematical model for this complex challenge, its formulation as a constrained, discrete optimization problem, and the first algorithmic approaches. It was also found that an evolutionary algorithm delivers higher-quality solutions than an ad-hoc heuristic, and faster running times compared to a well-known commercial solver. This paper introduces a memetic algorithm that exploits the strengths of the former evolutionary method while incorporating several key innovations: a domain-specific recombination operator; a specialized repairing procedure; an enhanced fitness evaluation scheme; and a multiobjective archiving strategy that preserves promising solution trade-offs. We conduct a comprehensive evaluation of the performance and behavior of this proposal, as well as the contribution of each specific design component. The results highlight that our memetic algorithm consistently outperforms previous approaches from the literature, providing better trade-offs in terms of solution quality and the rate at which vMNO requests are successfully fulfilled.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"55 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical heterogeneous graph learning for color-missing ALS pointcloud segmentation 用于缺色 ALS 点云分割的分层异构图学习

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-08-02 DOI: 10.1007/s12293-024-00426-2

Buliao Huang, Yunhui Zhu

{"title":"Hierarchical heterogeneous graph learning for color-missing ALS pointcloud segmentation","authors":"Buliao Huang, Yunhui Zhu","doi":"10.1007/s12293-024-00426-2","DOIUrl":"https://doi.org/10.1007/s12293-024-00426-2","url":null,"abstract":"Semantically segmented aerial laser scanning (ALS) pointcloud is crucial for remote sensing applications, offering advantages over aerial images in describing complex topography of vegetation-covered areas due to its ability to penetrate through vegetation. While many ALS pointcloud segmentation methods emphasize the importance of color information for accurate segmentation and colorize the ALS pointcloud with aerial images, they often overlook the fact that some points in vegetation-covered areas are occluded and cannot be observed in aerial images. Consequently, these methods may assign inaccurate colors to these points, resulting in degraded segmentation performance. To address this issue, this paper proposes a Hierarchical Heterogeneous Graph Learning (HHGL) algorithm. HHGL tackles the problem by treating the colors of occluded points (referred to as “color-missing points”) as missing values and compensating for them based on the local and global geometric relationships among color-missing points and color-observed points. Specifically, the proposed algorithm first models the local geometric relationships as a heterogeneous graph, which aggregates the features of adjacent color-observed points to make up for the missing colors. Additionally, the global geometric relationships are represented as a hierarchical structure, refining the aggregated features and capturing long-range dependencies among color-missing points to facilitate segmentation. Experimental results on real-world datasets validate the effectiveness and robustness of the proposed HHGL algorithm.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"78 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular representations in bio-cheminformatics 生物化学信息学中的分子表征

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-07-20 DOI: 10.1007/s12293-024-00414-6

Thanh-Hoang Nguyen-Vo, Paul Teesdale-Spittle, Joanne E. Harvey, Binh P. Nguyen

引用次数: 0

QEA-QCNN: optimization of quantum convolutional neural network architecture based on quantum evolution QEA-QCNN：基于量子进化的量子卷积神经网络架构优化

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-07-09 DOI: 10.1007/s12293-024-00417-3

Yangyang Li, Xiaobin Hao, Guanlong Liu, Ronghua Shang, Licheng Jiao

{"title":"QEA-QCNN: optimization of quantum convolutional neural network architecture based on quantum evolution","authors":"Yangyang Li, Xiaobin Hao, Guanlong Liu, Ronghua Shang, Licheng Jiao","doi":"10.1007/s12293-024-00417-3","DOIUrl":"https://doi.org/10.1007/s12293-024-00417-3","url":null,"abstract":"Quantum neural network (QNN) is a research orientation that combines quantum computing and machine learning. It has the potential to solve the bottleneck problem of shortage of computing resource in deep learning, and is expected to become the first practical application scheme that demonstrate application level quantum advantages on current Noise Intermediate scale Quantum (NISQ) devices. However, limited by the current scale of NISQ devices, QNNs have fewer quantum bits and quantum circuits cannot be too deep. Currently, there is no clear design strategy for the architecture of QNN. Designing QNN architectures arbitrarily not only has high circuit complexity but also often poor network performance. Similar to classical convolutional neural network, in this paper, a quantum evolution-based optimization algorithm is proposed for design of quantum convolutional neural network (QCNN) architecture. The design of QNN architecture is viewed as a combinatorial optimization problem, and the quantum evolution algorithm is used to adaptively design the QCNN architecture with its global search ability in a large discrete search space. Comprehensive experimental results indicate that the proposed method can effectively reduce the complexity of QCNN circuits, reduce the difficulty of deploying quantum circuits, and further improve the expressibility of QCNN.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"149 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively 通过对道路和驾驶员进行连续聚类，利用任意道路上的驾驶员轨迹进行驾驶员特征分析

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-07-09 DOI: 10.1007/s12293-024-00416-4

Shengfei Lyu, Di Wang, Xuehao Yang, Chunyan Miao

{"title":"Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively","authors":"Shengfei Lyu, Di Wang, Xuehao Yang, Chunyan Miao","doi":"10.1007/s12293-024-00416-4","DOIUrl":"https://doi.org/10.1007/s12293-024-00416-4","url":null,"abstract":"Driver profiling is a widely used tool in fleet management and driver-specific insurance because it differentiates drivers based on their driving behaviors, such as aggressive and non-aggressive, which correspond to different levels of driving risk. However, most existing driver profiling methods require all drivers to drive on the same predefined route or type of roads, simply to make sure their driving behaviors are comparable. This premise makes these methods not be able to profile drivers who drive on arbitrary roads, which constitute the real-world scenarios for most drivers. To enable the profiling of drivers using their naturalistic driving data, i.e., driving trajectories recorded while they were driving on arbitrary roads at their own free will, in this paper, we propose a novel method named cLustering rOads And Drivers Successively (LOADS). Specifically, LOADS first categorizes the roads into different types using the extracted characteristics of all drivers driving on the respective roads. It then groups drivers into different clusters to obtain their profile labels (e.g., aggressive or non-aggressive) using the extracted driving characteristics on each road type. We conduct extensive experiments using two real-world driving trajectory datasets comprising thousands of driving trajectories of hundreds of drivers. Statistical analysis results indicate that the driver groups identified by LOADS have significantly different driving styles. To the best of our knowledge, LOADS is the first method that focuses on profiling drivers who drive on arbitrary roads, showing a great potential to enable real-world driver profiling applications.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"16 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A distributed individuals based multimodal multi-objective optimization differential evolution algorithm 基于分布式个体的多模式多目标优化差分进化算法

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-06-20 DOI: 10.1007/s12293-024-00413-7

Wei Wang, Zhifang Wei, Tianqi Huang, Xiaoli Gao, Weifeng Gao

{"title":"A distributed individuals based multimodal multi-objective optimization differential evolution algorithm","authors":"Wei Wang, Zhifang Wei, Tianqi Huang, Xiaoli Gao, Weifeng Gao","doi":"10.1007/s12293-024-00413-7","DOIUrl":"https://doi.org/10.1007/s12293-024-00413-7","url":null,"abstract":"There may exist a one-to-many mapping between objective and decision spaces in multimodal multi-objective optimization problems (MMOPs), which requires the evolutionary algorithm to locate multiple non-dominated solution sets. In order to enhance the diversity of the population, we develop a multimodal multi-objective differential evolution algorithm based on distributed individuals and lifetime mechanism. First, every individual can be seen as a distributed unit to locate multiple non-dominated solutions. The solutions with the good diversity are generated by adopting virtual population, and the range of virtual population is adjusted by an adaptive adjustment strategy to locate more non-dominated solutions. Second, it is considered that each individual has a limited lifespan inspired by natural phenomenon. As the search area of individuals becoming adaptively smaller, the individuals with good quality are archived and they can reinitialize with a new lifespan for enhancing diversity of the search space. Then the probability selection strategy is applied in the environment selection to balance exploration and exploitation. The test results on 22 multimodal multi-objective benchmark test functions verify the superior performance of the proposed method.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"19 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two cooperative constraint handling techniques with an external archive for constrained multi-objective optimization 带外部档案的两种合作约束处理技术，用于约束多目标优化

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-04-20 DOI: 10.1007/s12293-024-00409-3

Jianlin Zhang, Jie Cao, Fuqing Zhao, Zuohan Chen

{"title":"Two cooperative constraint handling techniques with an external archive for constrained multi-objective optimization","authors":"Jianlin Zhang, Jie Cao, Fuqing Zhao, Zuohan Chen","doi":"10.1007/s12293-024-00409-3","DOIUrl":"https://doi.org/10.1007/s12293-024-00409-3","url":null,"abstract":"Constrained multi-objective problems are difficult for researchers to solve because they contain infeasible regions. To address this issue, this paper proposes two cooperative constraint handling techniques that use an external archive. First, two constraint handling techniques, i.e., the penalty function and the constrained dominance principle, are embedded in multi-objective optimization algorithms and work cooperatively on two populations to increase population diversity. Then, an external archive is designed to preserve high-quality solutions that strike a good balance between objectives, values, and constraints throughout the evolution process. Finally, comprehensive experiments are conducted to validate the performance of the proposed algorithm, and seven state-of-the-art constrained multi-objective optimization algorithms are used to compare three test suites and ten real-world problems. The experimental results demonstrate that the proposed algorithm can achieve competitive performance in solving various constrained multi-objective problems. Additionally, the results show that cooperative constraint handling techniques are more robust than single constraint handling methods.","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"82 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization 多模式多目标优化的邻域辅助进化算法

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-04-12 DOI: 10.1007/s12293-024-00410-w

Weiwei Zhang, Jiaqiang Li, Guoqing Li, Weizheng Zhang

引用次数: 0

A simple and scalable particle swarm optimization structure based on linear system theory 基于线性系统理论的简单可扩展粒子群优化结构

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2024-03-25 DOI: 10.1007/s12293-024-00408-4

Jian Zhu, Jianhua Liu

引用次数: 0