检索结果-内蒙古大学图书馆

A review of evolutionary multi-modal multi-objective optimization

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Tanabe, Ryoji Ishibuchi, Hisao Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space. Multi-modal multi-objective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multi-modal multi-objective optimization". To address this issue, this paper reviews existing studies of evolutionary multi-modal multi-objective optimization, including studies published under names that are different from "multi-modal multi-objective optimization". Our review also clarifies open issues in this research area. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

An easy-to-use real-world multi-objective optimization problem suite

学校读者我要写书评

暂无评论

arXiv 2020年

Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic properties which may lead to overestimation/underestimation. To address this issue, we present a multi-objective optimization problem suite consisting of 16 bound-constrained real-world problems. The problem suite includes various problems in terms of the number of objectives, the shape of the Pareto front, and the type of design variables. 4 out of the 16 problems are multi-objective mixed-integer optimization problems. We provide Java, C, and Matlab source codes of the 16 problems so that they are available in an off-the-shelf manner. We examine an approximated Pareto front of each test problem. We also analyze the performance of six representative evolutionary multi-objective optimization algorithms on the 16 problems. In addition to the 16 problems, we present 8 constrained multi-objective real-world problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

A niching indicator-based multi-modal many-objective optimizer

学校读者我要写书评

暂无评论

arXiv 2020年

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

Review and analysis of three components of differential evolution mutation operator in MOEA/D-DE

学校读者我要写书评

暂无评论

arXiv 2020年

A decomposition-based multi-objec evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective problems (MOPs). The DE mutation consists of three key components: a mutation strategy, an index selection method for parent individuals, and a bound-handling method. However, the configuration of the DE mutation operator that should be used for MOEA/D-DE has not been thoroughly investigated in the literature. This configuration choice confuses researchers and users of MOEA/D-DE. To address this issue, we present a review of the existing configurations of the DE mutation operator in MOEA/DDE and systematically examine the influence of each component on the performance of MOEA/D-DE. Our review reveals that the configuration of the DE mutation operator differs depending on the source code of MOEA/D-DE. In our analysis, a total of 30 configurations (three index selection methods, two mutation strategies, and five bound handling methods) are investigated on 16 MOPs with up to five objectives. Results show that each component significantly affects the performance of MOEA/D-DE. We also present the most suitable configuration of the DE mutation operator, which maximizes the effectiveness of MOEA/D-DE. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

An analysis of quality indicators using approximated optimal distributions in a Three-dimensional objective space

学校读者我要写书评

暂无评论

arXiv 2020年

Although quality indicators play a crucial role in benchmarking evolutionary multi-objective optimization algorithms, their properties are still unclear. One promising approach for understanding quality indicators is the use of the optimal distribution of objective vectors that optimizes each quality indicator. However, it is difficult to obtain the optimal distribution for each quality indicator, especially when its theoretical property is unknown. Thus, optimal distributions for most quality indicators have not been well investigated. To address these issues, first, we propose a problem formulation of finding the optimal distribution for each quality indicator on an arbitrary Pareto front. Then, we approximate the optimal distributions for nine quality indicators using the proposed problem formulation. We analyze the nine quality indicators using their approximated optimal distributions on eight types of Pareto fronts of three-objective problems. Our analysis demonstrates that uniformly-distributed objective vectors over the entire Pareto front are not optimal in many cases. Each quality indicator has its own optimal distribution for each Pareto front. We also examine the consistency among the nine quality indicators. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

A Multi-objective Computation Offloading Method for Hybrid Workflow Applications in Mobile Edge Computing 9th

学校读者我要写书评

暂无评论

A Multi-objective Computation Offloading Method for Hybrid W...

9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Peng, Kai Zhao, Bohai Qian, Xingda Xu, Xiaolong Zheng, Lixin Leung, Victor C. M. College of Engineering Huaqiao University Quanzhou China Fujian Provincial Academic Engineering Research Center in Industrial Intellectual Techniques and Systems Quanzhou China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9783030485122

Computation offloading has become a promising method to overcome intrinsic defects of portable smart devices, such as low operating speed and low battery capacity. However, it is a challenge to design an optimized strategy as the edge server is resource-constrained and the workflow application has timing constraints. In this paper, we investigated the hybrid workflow application computation offloading issue, which further increases the difficulty. According to the analysis of theory and consideration of time consumption and energy consumption, we establish a multi-objective optimization model to solve the issue. Furthermore, we propose a method based on particle swarm optimization algorithm for multi-objective computation offloading to get the optimal strategy for tasks offloading, which is suitable for all the hybrid workflow applications. Finally, extensive experiments have verified the effectiveness and efficiency of our proposed method. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Energy utilization

A framework to handle multi-modal multi-objective optimization in decomposition-based evolutionary algorithms

学校读者我要写书评

暂无评论

arXiv 2020年

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multi-objective optimization, they are likely to perform poorly for multi-modal multi-objective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this paper proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multi-modal multi-objective optimization. Our framework is based on three operations: assignment, deletion, and addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

Corrigendum to “LightSOD: Towards lightweight and efficient network for salient object detection” [J. Comput. Vis. Imag. Underst. 249 (2024) 104148]

学校读者我要写书评

暂无评论

computer Vision and Image Understanding 2025年 252卷

作者： Thien-Thu Ngo Hoang Ngoc Tran Md. Delowar Hossain Eui-Nam Huh School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Kristian IV:s väg 3 Halmstad 30118 Halland Sweden Department Computer Science and Engineering KyungHee University Global Campus Deogyeong-daero Yongin-si 17104 Gyeonggi-do Republic of Korea Department of Software Engineering FPT University 600 Nguyen Van Cu Street Ninh Kieu 94000 Can Tho Viet Nam Department of Computer Science and Engineering Hajee Mohammad Danesh Science & Technology University Basherhat Dinajpur 5200 Rangpur Division Bangladesh

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement