检索结果-内蒙古大学图书馆

2nd Asia Conference on Algorithms, Computing and Machine Learning (CACML)

作者： Wang, Mingjing Chen, Long Chen, Huiling Southeast Univ Nanjing Jiangsu Peoples R China Wenzhou Univ Wenzhou Peoples R China

ISBN: (纸本)9781450399449

many-objective optimization problems (MaOPs), are the most difficult problems to solve when it comes to multiobjective optimization issues (MOPs). MaOPs provide formidable challenges to current multiobjective evolutionary methods such as selection operators, computational cost, visualization of the high-dimensional trade-off front. Removal of the reductant objectives from the original objective set, known as objective reduction, is one of the most significant approaches for MaOPs, which can tackle optimization problems with more than 15 objectives is made feasible by its ability to greatly overcome the challenges of existing multi-objective evolutionary computing techniques. In this study, an objective reduction evolutionary multiobjective algorithm using adaptive density-based clustering is presented for MaOPs. The parameters in the density-based clustering can be adaptively determined by depending on the data samples constructed. Based on the clustering result, the algorithm employs an adaptive strategy for objective aggregation that preserves the structure of the original Pareto front as much as feasible. Finally, the performance of the proposed multiobjective algorithms on benchmarks is thoroughly investigated. The numerical findings and comparisons demonstrate the efficacy and superiority of the suggested multiobjective algorithms and it may be treated as a potential tool for MaOPs.

关键词： objective Reduction Evolutionary Multiobjective Algorithm Density-Based Clustering many-objective optimization problem

来源：评论

学校读者我要写书评

暂无评论

A hybrid many-objective optimization algorithm for coal green production problem

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6040-e6040页

作者： Cui, Zhihua Zhang, Jiangjiang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

The problem of convergence and diversity in the course of population evolution is difficult to be balanced for solving the many-objective optimization problem (MaOP). To track with the problem, a many-objective optimization algorithm is designed. In the algorithm, a hybrid selection mechanism under the concurrent integration strategy is built to improve algorithm performance by employing the different selection operators. The concurrent integration strategy can select the suitable operator to balance the convergence and diversity of the solution in the course of the population evolutionary. To verify the effectiveness of the algorithm, the designed algorithm is compared with other five excellent many-objective algorithms on DTLZ and WFG test problem. What is more, the designed algorithm is applied to solve the coal green production optimization problem. The simulation results show that the performance of designed algorithm is superior to whether the DTLZ and WFG test problem or the application problem.

关键词： coal green production optimization problem concurrent integration strategy convergence and diversity many-objective optimization problem

来源：评论

学校读者我要写书评

暂无评论

many-objective evolutionary algorithms based on reference-point-selection strategy for application in reactor radiation-shielding design

引用

NUCLEAR SCIENCE AND TECHNIQUES 2025年第6期36卷 1-15页

作者： Liu, Cheng-Wei Sun, Ai-Kou Lei, Ji-Chong Qu, Hong-Yu Yang, Chao Yu, Tao Chen, Zhen-Ping Univ South China Sch Nucl Sci & Technol Hengyang 421001 Peoples R China Univ South China Key Lab Adv Nucl Energy Design & Safety Minist Educ Hengyang 421000 Peoples R China

In recent years, the development of new types of nuclear reactors, such as transportable, marine, and space reactors, has presented new challenges for the optimization of reactor radiation-shielding design. Shielding structures typically need to be lightweight, miniaturized, and radiation-protected, which is a multi-parameter and multi-objective optimization problem. The conventional multi-objective (two or three objectives) optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters, as well as a deficiency in achieving a global optimal solution, thereby failing to meet the requirements of shielding optimization for newly developed reactors. In this study, genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective (having four or more objectives) optimal design of reactor radiation shielding. To validate the reliability of the methods, an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem. The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance, and they exhibit their reliability in practical engineering problems. The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes. Therefore, the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.

关键词： many-objective optimization problem Evolutionary algorithm Radiation-shielding design Reference-point-selection strategy

来源：评论

学校读者我要写书评

暂无评论

Extension of the Directed Search Domain algorithm for multi-objective optimization to higher dimensions

引用

ANNALS OF OPERATIONS RESEARCH 2024年第2-3期340卷 1125-1160页

作者： Yu, Boxi Utyuzhnikov, Sergey Univ Manchester Dept Fluids & Environm Manchester M13 9PL England

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases.

关键词： Multi-objective optimization Directed Search Domain Pareto set Evenly distributed set many-objective optimization problem

来源：评论

学校读者我要写书评

暂无评论

Novel PIO Algorithm with Multiple Selection Strategies for many-objective optimization problems

引用

Complex System Modeling and Simulation 2021年第4期1卷 291-307页

作者： Zhihua Cui Lihong Zhao Youqian Zeng Yeqing Ren Wensheng Zhang Xiao-Zhi Gao School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Computing University of Eastern FinlandKuopioFI-70211Finland

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.

关键词： pigeon-inspired optimization algorithm many-objective optimization problem multiple selection strategy elite individual retention

来源：评论

学校读者我要写书评

暂无评论

Solving the Food-Energy-Water Nexus problem via Intelligent optimization Algorithms 20

Solving the Food-Energy-Water Nexus Problem via Intelligent ...

引用

IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Deng, Qi Fan, Zheng Li, Zhi Pan, Xinna Kang, Qi Zhou, MengChu Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Google Dept Trust & Safety YouTube Mountain View CA 94043 USA Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread. However, the EA research community has not paid much attention to large-scale multi-objective optimization problems arising from real-world applications. Especially, Food-EnergyWater systems are intricately linked among food, energy and water that impact each other. They usually involve a huge number of decision variables and many conflicting objectives to be optimized. Solving their related optimization problems is essentially important to sustain the high-quality life of human beings. Their solution space size expands exponentially with the number of decision variables. Searching in such a vast space is challenging because of such large numbers of decision variables and objective functions. In recent years, a number of large-scale many-objectives optimization evolutionary algorithms have been proposed. In this paper, we solve a Food-Energy-Water optimization problem by using the state-of-art intelligent optimization methods and compare their performance. Our results conclude that the algorithm based on an inverse model outperforms the others. This work should be highly instrumental for practitioners to select the most suitable method for their particular large-scale engineering optimization problems.

关键词： Evolutionary algorithms Food-Energy-Water system many-objective optimization problem Intelligent optimization Inverse model Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

UAVs routes optimization on smart cities and regions

引用

RAIRO-OPERATIONS RESEARCH 2022年第2期56卷 853-869页

作者： Marques, Elias L., Jr. Coelho, Vitor N. Coelho, Igor M. Frota, Yuri A. de M. Koochaksaraei, Roozbeh H. Ochi, Luiz Satoru Coelho, Bruno N. Univ Fed Fluminense Inst Comp Av Gal Milton Tavares de Souza BR-24210310 Niteroi RJ Brazil OptBlocks Consultoria Ltda Ave Joao Pinheiro274 Sala 201 BR-30130186 Belo Horizonte MG Brazil George Washington Univ Comp Sci Dept Adjunct Fac Washington DC 20052 USA Univ Fed Ouro Preto Dept Control & Automat Engn Campus Morro do Cruzeiro BR-35400000 Ouro Preto MG Brazil

Unmanned Aerial Vehicles are becoming a common technology used on smart cities and smart regions, thus requiring optimization of its routes with crucial importance. In this innovative work, six objective functions are optimized in order to provide sets of non-dominated solutions, composed of routes with different characteristics. Realistic constraints are considered such as obstacles and areas in which drones could not pass through. A didactic case of study considering points of a graph is used in order to illustrate a smart city composed of different regions. Obtained solutions are analyzed using a state-of-the-art visualization tool, which guides the comprehension of harmony and conflicts between objectives.

关键词： Smart City UAVs routing problem MILP many-objective optimization problem

来源：评论

学校读者我要写书评

暂无评论

many-objective approach based on problem-aware mutation operators for protein encoding

引用

INFORMATION SCIENCES 2022年 613卷 376-400页

作者： Diaz-Galian, Maria Victoria Vega-Rodriguez, Miguel A. Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

One of the research pathways in synthetic biology is protein encoding, which aims to improve and increase protein expression. The production of heterologous proteins is important in different fields, such as medicine, environmental biology, and agriculture. Protein encoding is a difficult task and can be defined as a many-objective optimization problem, where four objectives must be optimized concurrently: codon adaptation, guanine-cytosine content, difference between sequences, and avoidance of hairpin loops. These objectives are why we propose and describe a many-objective approach based on the non-dominated sorting genetic algorithm-III (NSGA-III) for protein encoding. The proposed algorithm was designed to work with novel mutation operators that are problem-aware and consider the different optimization objectives. The results of the pro-posed algorithm were compared to those of different tools that were reported by other authors to determine its efficiency. Comparisons were made using several quality metrics and with statistical analyses. The average improvements in hypervolume and set coverage were between 5.34% and 55.75%, and 53.01% and 100%, respectively. Based on the statisti-cal analyses, the proposed algorithm was found to produce statistically significant improvements compared to other algorithms, thus highlighting the relevance of the proposed approach for protein encoding.(c) 2022 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problem Non-dominated Sorting Genetic Algorithm-III problem-aware mutations Bioinformatics Protein encoding

来源：评论

学校读者我要写书评

暂无评论

An agent-based cooperative co-evolutionary framework for optimizing the production planning of energy supply chains under uncertainty scenarios

引用

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2024年 277卷

作者： Chen, Shiyu Ma, Chiye Wang, Wei Zio, Enrico Chengdu Univ Informat Technol Sch Logist Chengdu Peoples R China City Univ Hong Kong Dept Mech Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China Politecn Milan Dept Energy Via La Masa 34 I-20156 Milan Italy PSL Univ Paris Ctr Rech Risques & Crises CRC MINES ParisTech Sophia Antipolis France

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a many-objective optimization problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events. For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

关键词： Energy supply chain Production planning Uncertainty many-objective optimization problem Agent-based modeling Co-evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

An adaptive interval many-objective evolutionary algorithm with information entropy dominance

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Cui, Zhihua Qu, Conghong Zhang, Zhixia Jin, Yaqing Cai, Jianghui Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China North Univ China Sch Comp Sci & Technol Taiyuan 030051 Shanxi Peoples R China Chinese Acad Sci State Key Lab Intelligent Control & Management Com Inst Automat Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

Interval many-objective optimization problems (IMaOPs) involve more than three conflicting objectives with interval parameters. Various real-world applications under uncertainty can be modeled as IMaOPs to solve, so effectively handling IMaOPs is crucial for solving practical problems. This paper proposes an adaptive interval many-objective evolutionary algorithm with information entropy dominance (IMEA-IED) to tackle IMaOPs. Firstly, an interval dominance method based on information entropy is proposed to adaptively compare intervals. This method constructs convergence entropy and uncertainty entropy related to interval features and innovatively introduces the idea of using global information to regulate the direction of local interval comparison. Corresponding interval confidence levels are designed for different directions. Additionally, a novel niche strategy is designed through interval population partitioning. This strategy introduces a crowding distance increment for improved subpopulation comparison and employs an updated reference vector method to adjust the search regions for empty subpopulations. The IMEA-IED is compared with seven interval optimization algorithms on 60 interval test problems and a practical application. Empirical results affirm the superior performance of our proposed algorithm in tackling IMaOPs.

关键词： Evolutionary algorithm Interval uncertainty Interval dominance method Information entropy many-objective optimization problem Niche selection strategy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：