检索结果-内蒙古大学图书馆

A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model

引用

COMPUTATIONAL GEOSCIENCES 2019年第6期23卷 1219-1235页

作者： Ticona Gutierrez, Juan Carlos Adamatti, Daniela Santini Bravo, Juan Martin Univ Fed Rio Grande do Sul Inst Pesquisas Hidraul Porto Alegre RS Brazil

multi-objective genetic algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to stop the search. Many users commonly use rules like stopping when there is no significant improvement during the last generations or when a certain number of generations are reached. In this paper, we propose a new stopping criterion approach and evaluate its performance with three widely used evolutionary algorithms in the calibration of a hydrologic model. The stopping criterion is based on the minimum number of generations required to achieve a determined number of non-dominated solutions in Pareto Front. The new stopping criterion was tested in the lumped hydrologic model IPH-II calibration, using the genetic algorithms NSGA-II, NSGA-III, and SPEA-II and two objective functions. The generational distance, spacing, and maximum spread metrics were used to assess the performance of the proposed stopping criterion in comparison to the standard criterion. Results show no significant loss in goodness of fit associated with the proposed stopping criterion, both in calibration and validation periods. Performance metrics have shown similar values when the standard and the proposed stopping criteria were compared. However, the average computational time to complete the optimization process was reduced up to 38.2% when the proposed stopping criterion was used. Thus, it can be concluded that the new stopping criterion reduces the iteration workload without compromising the accuracy of solution sets.

关键词： multi-objective evolutionary algorithm Lumped hydrologic model Stopping criterion NSGA-II NSGA-III SPEA-II

来源：评论

学校读者我要写书评

暂无评论

Do Search and Selection Operators Play Important Roles in multi-objective evolutionary algorithms:A Case Study

引用

Wuhan University Journal of Natural Sciences 2003年第S1期8卷 195-201页

作者： Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China School of Computer Science UC UNSW Australian Defence Force Academy Canberra Australia Capital Bridge Securities Co. Ltd Shanghai China

multi-objective evolutionary algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete multi-objective Optimization Problem (MOP): multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

关键词： multi-objective evolutionary algorithm convergence property analysis search operator selection operator Markov chain

来源：评论

学校读者我要写书评

暂无评论

A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Zhang, Yajie Wu, Chengming Tian, Ye Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China

Sparse large-scale multi -objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi -objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co -evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem.

关键词： multi-objective evolutionary algorithm Large-scale multi-objective optimization Signal reconstruction Community detection Instance selection

来源：评论

学校读者我要写书评

暂无评论

A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 121卷

作者： Chai, Zheng-Yi Liu, Xu Li, Ya-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Software Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory -based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.

关键词： Computation offloading Dynamic multi-objective optimization Mobile edge computing multi-objective evolutionary algorithm (MOEA)

来源：评论

学校读者我要写书评

暂无评论

NSGA-II/EDA Hybrid evolutionary algorithm for Solving multi-objective Economic/Emission Dispatch Problem

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第10期46卷 1158-1170页

作者： Alawode, Kehinde Olukunmi Adegboyega, Gabriel Adebayo Muhideen, Jubril Abimbola Osun State Univ Dept Elect & Elect Engn Osogbo Nigeria Obafemi Awolowo Univ Dept Elect & Elect Engn Ife Nigeria

In this study, a hybrid algorithm which combines the NSGA-II with a modified form of the marginal histogram model Estimation of Distribution algorithm (EDA), herein called the NSGA-II/EDA is proposed for solving the multi-objective economic/emission power dispatch problem. The goal is to improve the convergence while preserving the diversity properties of the obtained solution set. The effect of variable interaction on the marginal histogram EDA model is reduced by performing multi-scale Principal Component Analysis on the population of solutions. Also, the concepts of non-domination and elitism have been introduced into the marginal histogram model in order for it to handle multiple objectives. Several optimization runs were carried out on the standard multi-objective test problems, including the IEEE 30- and the 118-bus test systems. Standard metrics are used to compare the performance of the developed hybrid approach with that of other multi-objective evolutionary algorithms. The effectiveness of the proposed approach in improved convergence, with good diversity is demonstrated.

关键词： multi-objective evolutionary algorithm hybrid evolutionary algorithm combined economic-emission dispatch problem

来源：评论

学校读者我要写书评

暂无评论

Optimization of a Demand Responsive Transport Service Using multi-objective evolutionary algorithms 19

Optimization of a Demand Responsive Transport Service Using ...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Viana, Renan J. S. Santos, Andre G. Martins, Flavio V. C. Wanner, Elizabeth F. CEFET MG Programa Posgrad Modelagem Matemat & Computac Belo Horizonte MG Brazil Univ Fed Vicosa Vicosa MG Brazil Ctr Fed Educ Tecnol Minas Gerais Belo Horizonte MG Brazil Aston Univ Birmingham W Midlands England

ISBN: (纸本)9781450367486

This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desired locations and times of pick-up and delivery. The vehicle routing and scheduling procedures are performed based on a set of requests. This problem is modeled as a multi-objective Dial-a-Ride problem (DARP), in which a set of objectives related to costs and user inconvenience is optimized while respecting a set of constraints imposed by the passengers and vehicles, as time windows and capacity. The resulting model is solved by means of three multi-objective evolutionary algorithms (MOEA) associated with feasibility-preserving operators. Computational experiments were performed on benchmark instances and the results were analyzed by means of performance quality indicators widely used for multi-objective algorithms comparison. The proposed approaches demonstrate efficient and higher performance when optimizing this DRT service compared to another algorithm from the literature.

关键词： Demand Responsive Transport Dial-a-Ride Problem multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING multi-objective evolutionary algorithmS 14

BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING MULTI-OB...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Golchin, Maryam Davarpanah, Seyed Hashem Liew, Alan Wee-Chung Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia

ISBN: (纸本)9781467372206

Clustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subsetof conditions. Biclustering allows clustering of rows and columns of a dataset simultaneously. It extracts more accurate information from sparse datasets. In recent years, biclustering has found many useful applications in different fields and many biclustering algorithms have been proposed. Using both row and column information of data, biclustering requires the optimization of two conflicting objectives. In this study, a new multi-objective evolutionary biclustering framework using SPEA2 is proposed. A heuristic local search based on the gene and condition deletion and addition is added into SPEA2 and the best bicluster is selected usinga new quantitative measure that considers both its coherence and size. The performance of our algorithm is evaluatedusing simulated and gene expression data and compared with several well-known biclustering methods. The experimental results demonstrate better performance with respect to the size and MSR of detected biclusters and significant enrichment of detected genes.

关键词： Biclustering Gene expression data multi-objective evolutionary algorithm SPEA2

来源：评论

学校读者我要写书评

暂无评论

Sizing mixed-mode circuits by multi-objective evolutionary algorithms

Sizing mixed-mode circuits by multi-objective evolutionary a...

引用

53rd Midwest Symposium on Circuits and Systems (MWSCAS 2010)

作者： Guerra-Gomez, I. Tlelo-Cuautle, E. McConaghy, Trent de la Fraga, Luis G. Gielen, Georges Reyes-Salgado, G. Munoz-Pacheco, J. M. INAOE Department of Electronics. M&#x00E9 xico

ISBN: (纸本)9781424477739

We show the behavior of the generations of two multi-objective evolutionary algorithms (MOEAs) for the optimal sizing of two mixed-mode circuits. The non-sorting genetic algorithm (NSGA-II), and the MOEA based on decomposition (MOEA/D) are used to size a second generation current conveyor (CCII+) and a current-feedback operational amplifier (CFOA). Both MOEAs take into account design constraints, and link HSPICE to evaluate the electrical characteristics of the CCII+ and CFOA. Differential evolution is used as genetic operator to show the behavior of the generations of the two MOEAs.

关键词： current conveyors SIZING evolutionary algorithm Hybrid modes design constraints multi-objective evolutionary algorithm Electric circuits ELECTRICAL CHARACTERISTICS current feedback operational amplifier Genetic Operator

来源：评论

学校读者我要写书评

暂无评论

Edge-Rotated Cone Orders in multi-objective evolutionary algorithms for Improved Convergence and Preference Articulation

Edge-Rotated Cone Orders in Multi-objective Evolutionary Alg...

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wang, Yali Deutz, Andre Back, Thomas Emmerich, Michael Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781728125473

The Pareto dominance relation is a special case of a cone order. Cone orders and the Pareto order are translation invariant and also multiplication invariant by any positive real. Employment of more general cone orders instead of the Pareto order gives rise to interesting exploration opportunities for algorithm design. In this paper, the standard Pareto dominance relation has been extended to cone dominance with a pointed convex obtuse cone, a superset of the Pareto dominance cone, i.e., the non-negative orthant, by rotating the edges of the Pareto order cone. The basic idea is in line with earlier work on coneorders in multicriteria decision nuking and here, in particular, the cone-order is used (1) to increase solutions' dominance area and hence improve the convergence speed of the algorithm and (2) to formulate trade-off constraints. The minima of the obtuse cone order are also Pareto optimal. However. not all minima of the Pardo order are also minima of the obtuse cone order. Therefore we use the edge-rotated cone in an alternating manner with the standard Pareto cone to guarantee coverage of the entire Pareto front. The edge-rotated cones have been integrated in several state-of-the-art multi-objective evolutionary algorithms (MOEAs) and proven to be able to improve the performance of MOEAs on multi-objective optimization problems with the linear, concave, convex and disconnected Pareto front. Furthermore, when the edges of the cone are rotated by different angles, the algorithm obtains solution sets located in different areas of the Pareto front. This behavior can be used to take the preference of the targeted region on the Pareto front into the algorithm. In particular, by using obtuse cones, regions where trade-off is very unbalanced can be discarded. This is quantified by showing a relationship between the angle and the trade-off rate corresponding to this angle.

关键词： Cone order Pareto domination multi-objective evolutionary algorithm preference-based optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts 06

Comparison of multi-objective evolutionary algorithms in opt...

引用

8th Annual Genetic and evolutionary Computation Conference

作者： Oesterreicher, Ingo Mitschele, Andreas Schlottmann, Frank Seese, Detlef Univ Karlsruhe TH Inst AIFB D-76128 Karlsruhe Germany GILLARDON AG Financial Software D-75015 Bretten Germany

ISBN: (纸本)9781595931863

Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two state-of-the-art MOEAs are used to perform an optimization of yet unresolved problem instances. In addition to that, we focus on finding a dense set of solutions to derive analogies to theoretic results of easier problem instances.

关键词： multi-objective evolutionary algorithm optimal reinsurance mean-variance-criterion value-at-risk

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：