检索结果-内蒙古大学图书馆

An enhanced diversity indicator-based many-objective evolutionary algorithm with shape-conforming convergence metric

引用

APPLIED SOFT COMPUTING 2024年 166卷

作者： Cao, Jiale Yang, Lei Li, Kangshun Zhang, Yuanye Tian, Jinglin Wang, Dongya South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

As the number of objectives increases, many-objective optimization problems (MaOPs) become increasingly complex. Traditional indicator-based many-objective evolutionary algorithms can often ensure the convergence of the population but tend to struggle with maintaining its diversity. In this paper, an enhanced diversity indicator-based many-objective evolutionary algorithm with shape-conforming convergence metric, namely, MaOEA-DISC, is presented to relieve this weakness. In MaOEA-DISC, firstly, we focus on the inter-individual spacing relationships within the population based on I-& varepsilon;+ Indicator, proposing a novel enhanced diversity I-& varepsilon;+ Indicator to ensure the enhancement of population diversity while converging. Secondly, we propose a new metric of individual convergence, which calculates the convergence of individuals based on the shape of Pareto front, reducing the errors caused by the shape of the Pareto front when measuring individual convergence, thereby assessing individual convergence more accurately. Finally, to further improve the convergence speed of the population, different mating strategies are employed for mating in the parental generation. MaOEA-DISC is compared with other algorithms on various benchmark MaOPs ranging from 3 to 10 objectives, as well as a real-world MaOPs. Experimental results demonstrate that when dealing with MaOPs, MaOEA-DISC not only achieves excellent population convergence and diversity but also effectively maintains a balance between them, showing promising practical value.

关键词： many-objective evolutionary algorithm Enhanced diversity I indicator Shape-conforming convergence metric Mating strategy

来源：评论

学校读者我要写书评

暂无评论

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第1期16卷 80-96页

作者： Lan, Yang Xie, Lijie Cai, Xingjuan Wang, Lifang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

关键词： integrated strategy intelligent medicine many-objective evolutionary algorithm skin cancer

来源：评论

学校读者我要写书评

暂无评论

Communication-efficient federated recommendation model based on many-objective evolutionary algorithm

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Cui, Zhihua Wen, Jie Lan, Yang Zhang, Zhixia Cai, Jianghui Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China

The federated recommendation system (FedRS), which is the application of the recommendation system (RS) in federated learning, has been creatively developed as increasing attention has been paid to user privacy protection. However, traditional federated learning consumes excessive communication time and resource, which seriously limits the development of the FedRS. To reduce the communication cost and improve the recommendation performance of FedRS, an improved many-objective federated recommendation model with a novel parameter reduction strategy is proposed in this paper. The model aims to optimize the number of parameters shared between the server and client to improve communication efficiency in the federated process. Optimal parameter selection solutions can be obtained using the many-objective evolutionary algorithm (MaOEA), which can optimize the recommendation accuracy, novelty, diversity, and communication efficiency of FedRS simultaneously. Furthermore, the reference vector guided evolutionary algorithm (RVEA) was adopted to evaluate the proposed model. Experiments were performed on two famous recommendation datasets to examine the superiority of RVEA for the evaluation. The performance results indicated that the proposed model can not only provide accurate, diverse, and novel recommendations for the client, but can also realize efficient communication.

关键词： Federated learning Recommendation system Federated recommendation model many-objective evolutionary algorithm RVEA

来源：评论

学校读者我要写书评

暂无评论

A hybrid many-objective evolutionary algorithm for flexible job-shop scheduling problem with transportation and setup times

引用

COMPUTERS & OPERATIONS RESEARCH 2021年 132卷 105263-105263页

作者： Sun, Jinghe Zhang, Guohui Lu, Jiao Zhang, Wenqiang Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China

This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural characteristics of the problem and develop a new neighborhood structure. The neighborhood structure defines four move types for different objectives. Next, we propose a hybrid many-objective evolutionary algorithm (HMEA), which is designed to better balance exploitation and exploration. In this algorithm, the tabu search with the neighborhood structure is proposed to improve the local search ability. A reference-point based non-dominated sorting selection is presented to guide the algorithm to search towards the Pareto-optimal front and maintain diversity of solutions. Through three sets of experiments based on 28 benchmark instances, the partial and overall effects of this algorithm are evaluated. The experimental results demonstrate the effectiveness of the proposed HMEA in solving the MaOFJSPT/S.

关键词： many-objective evolutionary algorithm Flexible job-shop scheduling problem Transportation and setup times Neighborhood structure Reference-point

来源：评论

学校读者我要写书评

暂无评论

A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

引用

SWARM AND evolutionary COMPUTATION 2023年 77卷

作者： Cui, Zhihua Xue, Zhaoyu Fan, Tian Cai, Xingjuan Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China

This paper designs a many-objective optimized edge and terminal collaborative computation offloading (MaOETCCO) model to give full play to the performance advantages of collaboration and resource sharing be-tween edge server (ES) and intelligent terminal equipment (ITE) for computation offloading, which considers three offloading levels and four objectives: offloading delay minimization, application execution time minimi-zation, energy consumption minimization, and most efficient load balancing. At the same time, a many-objective evolutionary algorithm with load constraints (MaOEA-LC) is designed to solve the model by obtaining Pareto optimal solutions. It consists of a two-layer tournament mating selection based on load constraints, an envi-ronment selection method based on objective value domination, and an evolutionary method of double popu-lation crossover and multipoint mutation. In simulations, the values of the convergence performance evaluation index GD and diversity performance evaluation index Spa are the best in the comparison with other evolutionary algorithms. With optimization of MaOEA-LC, the MaOETCCO model is solved faster with more diverse solutions, and the objective values in the model have been optimized by 31%, 8%, 16%, and 30%, respectively. In addition, the rank value and adjusted p-value of the Friedman statistical test are used to prove the significant advantage of MaOEA-LC.

关键词： Load balancing many-objective evolutionary algorithm Collaborative computation offloading Edge computing

来源：评论

学校读者我要写书评

暂无评论

A many-objective evolutionary algorithm based on Directional Diversity and Favorable Convergence

A Many-Objective Evolutionary Algorithm based on Directional...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Jixiang Cheng Gary G. Yen Gexiang Zhang School of Electrical Engineering Southwest Jiaotong University School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781479938414

The performances of Pareto-based multi-objective evolutionary algorithms deteriorate severely when solving many-objective optimization problems (MaOPs) mainly due to the loss of selection pressure and inappropriate design in diversity maintenance mechanism. To handling MaOPs, this paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity and favorable convergence (MaOEA-DDFC). In the algorithm, the mating selection based on favorable convergence and Pareto-dominance is applied to strengthen the selection pressure while an environmental selection considering directional diversity and favorable convergence is designed in order to make a good trade-off between diversity and convergence. To validate algorithm performance, seven DTLZ problems with 3, 5, 7 and 10 objectives are tested. Experimental results show that the proposed MaOEA-DDFC performs better than five state-of-the-art MaOEAs in terms of inverted generational distance and hypervolume indicators.

关键词： many-objective evolutionary algorithm Directional diversity Favorable convergence

来源：评论

学校读者我要写书评

暂无评论

A Novel objective Grouping evolutionary algorithm for many-objective Optimization Problems

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第6期34卷

作者： Guo, Xiaofang Wang, Xiaoli Xian Technol Univ Sch Sci Xuefu Rd Xian Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol South Taibai Rd Xian Shaanxi Peoples R China

The thorniest difficulties for multi-objective evolutionary algorithms (MOEAs) handling many-objective optimization problems (MaOPs) are the inefficiency of selection operators and high computational cost. To alleviate such difficulties and simplify the MaOPs, objective reduction algorithms have been proposed to remove the redundant objectives during the search process. However, those algorithms can only be applicable to specific problems with redundant objectives. Worse still, the Pareto solutions obtained by reduced objective set may not be the Pareto solutions of the original MaOPs. In this paper, we present a novel objective grouping evolutionary algorithm (OGEA) for general MaOPs. First, by dividing original objective set into several overlapping lower-dimensional subsets in terms of interdependence correlation information, we aim to separate the MaOPs into a number of sub-problems so that each of them can be able to preserve as much dominance structure in the original objective set as possible. Subsequently, we employ the nondominated sorting genetic algorithm II (NSGA-II) to generate Pareto solutions. Besides, instead of nondominated sorting on the whole population, a novel dual selection mechanism is proposed to choose individuals either having high ranks in subspaces or locating sparse region in the objective space for better proximity and diversity. Finally, we compare the proposed strategy with the other two classical space partition methods on benchmark DTLZ5 (I, M), DTLZ2 and a practical engineering problem. Numerical results show the proposed objective grouping algorithm can preserve more dominance structure in original objective set and achieve better quality of Pareto solutions.

关键词： many-objective evolutionary algorithm objective grouping interdependence relation dual selection mechanism

来源：评论

学校读者我要写书评

暂无评论

evolutionary many-objective algorithm with improved growing neural gas and angle-penalized distance for irregular problems

引用

APPLIED INTELLIGENCE 2023年第17期53卷 19892-19921页

作者： Gu, Qinghua Pang, Dejun Wang, Qian Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & De Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China

For solving complex many-objective optimization problems, the many-objective evolutionary algorithms may require new distance measures and more enormous selection pressures to guide solutions to the Pareto front. In this case, solving irregular Pareto front problems with growing neural gas networks as reference vectors is a great challenge. This paper proposes an algorithm with improved growing neural gas and angle-penalized distance to overcome the difficulty: First, a new parameter, the proportion of the solutions relatively close to the input data, is introduced to the angle-penalty distance, boosting the influence on the entire environment selection and enhancing the effect for Pareto front learning. Second, the edge-acceleration strategy enhances the ability of growing neural gas to match Pareto fronts and adapt to rapidly changing input. Finally, by adjusting the nodes deletion strategy in the search procedure, more promising solutions may be maintained to optimize the solutions' distribution. In experiments tackling problems with irregular Pareto fronts, the proposed algorithm performs competitively in contrast to the other six excellent algorithms.

关键词： many-objective evolutionary algorithm Irregular Pareto front Growing neural gas Angle-penalized distance

来源：评论

学校读者我要写书评

暂无评论

Evaluation of many-objective evolutionary algorithms by Hesitant Fuzzy Linguistic Term Set and Majority Operator

引用

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第6期20卷 2043-2056页

作者： Yu, Xiaobing Lu, Yiqun Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Res Ctr Prospering Jiangsu Prov Talents Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol China Inst Mfg Developing Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China

Over the past few decades, many-objective evolutionary algorithms have been proposed and presented as competitive compared with state-of-the-art algorithms. The evaluation of these algorithms involves many performance metrics, considered as a multiple criteria decision-making problem. In order to fairly and faithfully evaluate these algorithms, a novel evaluation approach based on hesitant fuzzy linguistic term set and majority operator is proposed. Hesitant fuzzy linguistic term set is used to express the opinions of experts, and majority operator is used to aggregate the opinions of experts. The framework for evaluation is presented, in which comprehensive performance metrics are proposed. An experimental study is designed to validate the proposed method. The experimental results indicate that the proposed approach is accurate and effective;the ability of algorithms to solve many-objective problems relies on both algorithms and the features of problems. Finally, the proposed method is used to evaluate the meteorological disaster that occurred in China in 2008.

关键词： many-objective evolutionary algorithm Hesitant fuzzy linguistic term set Majority Evaluation Meteorological disaster

来源：评论

学校读者我要写书评

暂无评论

Ensemble of many-objective evolutionary algorithms for many-objective problems

引用

SOFT COMPUTING 2017年第9期21卷 2407-2419页

作者： Zhou, Yalan Wang, Jiahai Chen, Jian Gao, Shangce Teng, Luyao Sun Yat Sen Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Guangdong Univ Finance & Econ Coll Informat Guangzhou Guangdong Peoples R China Toyama Univ Dept Intellectual Informat Engn Fac Engn Toyama Japan Victoria Univ Coll Engn & Sci Melbourne Vic Australia

The performance of most existing multiobjective evolutionary algorithms deteriorates severely in the face of many-objective problems. many-objective optimization has been gaining increasing attention, and many new many-objective evolutionary algorithms (MaOEA) have recently been proposed. On the one hand, solution sets with totally different characteristics are obtained by different MaOEAs, since different MaOEAs have different convergence-diversity tradeoff relations. This may suggest the potential usefulness of ensemble approaches of different MaOEAs. On the other hand, the performance of MaOEAs may vary greatly from one problem to another, so that choosing the most appropriate MaOEA is often a non-trivial task. Hence, an MaOEA that performs generally well on a set of problems is often desirable. This study proposes an ensemble of MaOEAs (EMaOEA) for many-objective problems. When solving a single problem, EMaOEA invests its computational budget to its constituent MaOEAs, runs them in parallel and maintains interactions between them by a simple information sharing scheme. Experimental results on 80 benchmark problems have shown that, by integrating the advantages of different MaOEAs into one framework, EMaOEA not only provides practitioners a unified framework for solving their problem set, but also may lead to better performance than a single MaOEA.

关键词： many-objective evolutionary algorithm Ensemble algorithm portfolios many-objective optimization

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：