检索结果-内蒙古大学图书馆

17th International Conference on Neural Information Processing

作者： Ripon, Kazi Shah Nawaz Glette, Kyrre Hovin, Mats Torresen, Jim Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9783642175367

In today's volatile manufacturing scenario, manufacturing facilities must operate in a dynamic and market-driven environment in which production rates and production mixes are continuously changing. To operate efficiently within such an environment, the facility layout needs to be adaptable to changes. The dynamic facility layout problem (DFLP) deals with changes of layout over time. DFLPs are usually solved just considering quantitative aspect of layout alone, ignoring the qualitative aspect. Few attempts have been made to date to deal with the multi-objective DFLP. These most often use the weighted-sum method to combine different objectives and thus, inherit the well-known problems of this method. The objective of this paper is to introduce an evolutionary approach for solving multi-objective DFLP that presents the layout as a set of Pareto-optimal solutions optimizing both quantitative and qualitative objectives simultaneously. Experimental results obtained with the proposed approach are promising.

关键词： Pareto-optimal solutions Dynamic facility layout problem multi-objective evolutionary optimization

来源：评论

学校读者我要写书评

暂无评论

Parallel Dual Broad Learning Surrogate Assisted Semi-supervised Kernel multi-objective evolutionary Rough Fuzzy Clustering Algorithm for Image Segmentation 6

Parallel Dual Broad Learning Surrogate Assisted Semi-supervi...

引用

6th International Conference on Natural Language Processing (ICNLP)

作者： Du, Weilin Zhao, Feng Xian Univ Posts & Telecommun Sch Commun & Informat Engn Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

To improve the segmentation performance of multi-objective evolutionary clustering algorithms, this paper proposes a parallel dual broad learning surrogate assisted semi-supervised kernel multi-objective evolutionary rough fuzzy clustering algorithm (PDBLS-SKMRFC) for image segmentation. First, the algorithm constructs a parallel dual broad learning surrogate assisted multi-objective evolutionary framework. It uses two broad learning systems as classification and regression surrogate models to evaluate the population in parallel manner. In the framework, a multi-population division strategy guided by dual broad learning system, a dominant individual crossover strategy, a sub-population mutation strategy, and a hierarchical environment selection strategy are designed to obtain more excellent offspring populations. Then, a semi-supervised kernel rough fuzzy intra-class compactness function is constructed, which uses a few labels and pseudo-labels as supervision information to improve the image segmentation performance, and evaluates the clustering quality together with the kernel separability function. Finally, a semi-supervised kernel rough fuzzy clustering validity index is designed to select the optimal solution from the final non-dominated solution set for image segmentation. Experimental results on color images show the effectiveness of the proposed algorithm.

关键词： image segmentation rough fuzzy clustering multi-objective evolutionary optimization surrogate assisted optimization semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Auto-adaptation of Genetic Operators for multi-objective optimization in the Firefighter Problem

引用

15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (纸本)9783319108407;9783319108391

In the firefighter problem the spread of fire is modelled on an undirected graph. The goal is to find such an assignment of firefighters to the nodes of the graph that they save as large part of the graph as possible. In this paper a multi-objective version of the firefighter problem is proposed and solved using an evolutionary algorithm. Two different auto-adaptation mechanisms are used for genetic operators selection and the effectiveness of various crossover and mutation operators is studied.

关键词： operator auto-adaptation multi-objective evolutionary optimization graph-based optimization firefighter problem

来源：评论

学校读者我要写书评

暂无评论

The Sim-EA Algorithm with Operator Autoadaptation for the multiobjective Firefighter Problem 1

引用

15th European Conference on evolutionary Computation in Combinatorial optimization (EvoCOP)

作者： Michalak, Krzysztof Wroclaw Univ Econ Dept Informat Technol Inst Business Informat Wroclaw Poland

ISBN: (数字)9783319164687

ISBN: (纸本)9783319164687;9783319164670

The firefighter problem is a graph-based optimization problem that can be used for modelling the spread of fires, and also for studying the dynamics of epidemics. Recently, this problem gained interest from the softcomputing research community and papers were published on applications of ant colony optimization and evolutionary algorithms to this problem. Also, the multiobjective version of the problem was formulated. In this paper a multipopulation algorithm Sim-EA is applied to the multiobjective version of the firefighter problem. The algorithm optimizes firefighter assignment for a predefined set of weight vectors which determine the importance of individual objectives. A migration mechanism is used for improving the effectiveness of the algorithm. Obtained results confirm that the multipopulation approach works better than the decomposition approach in which a single specimen is assigned to each direction. Given less computational resources than the decomposition approach, the Sim-EA algorithm produces better results than a decomposition-based algorithm.

关键词： multipopulation algorithms multi-objective evolutionary optimization Graph-based optimization Firefighter problem

来源：评论

学校读者我要写书评

暂无评论

Optimized design of induction heating coils: Technology-adapted solution

Optimized design of induction heating coils: Technology-adap...

引用

19th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF)

作者： Di Barba, Paolo Dughiero, Fabrizio Forzan, Michele Lupi, Sergio Mognaschi, Maria Evelina Sieni, Elisabetta Pavia Univ Dept Elect Comp & Biomed Engn Via Ferrata 5 I-27100 Pavia Italy Padova Univ Dept Ind Engn Padua Italy Insubria Univ Dept Theoret & Appl Sci Varese Italy

In industrial manufacturing process for tube production the stress relieving is achieved by heating the part to be treated at a very uniform temperature. In the paper, the optimized design of an inductor for stress relieving at temperature close to 600 degrees is considered. The design of the inductor is initially performed by means an automated-design using optimization algorithms and objective functions that are minimized. In the range of obtained solutions, some of them appear more promising in terms of heating uniformity. Starting from these improved solutions, a technologically acceptable solution is proposed. This technological feasible solution is selected between the ones obtained by means of a multi-objective optimization procedure and suitably adapted in order to achieve not only the technological targets but also a cost-effective design.

关键词： multi-objective evolutionary optimization multi-physics analysis finite elements induction heating

来源：评论

学校读者我要写书评

暂无评论

Enhanced Analog and RF IC Sizing Methodology using PCA and NSGA-II optimization Kernel

Enhanced Analog and RF IC Sizing Methodology using PCA and N...

引用

Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Pessoa, Tiago Lourenco, Nuno Martins, Ricardo Povoa, Ricardo Horta, Nuno Univ Lisbon Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9783981926309

State-of-the-art design of analog and radio frequency integrated circuits is often accomplished using sizing optimization. In this paper, an innovative combination of principal component analysis (PCA) and evolutionary computation is used to increase the optimizer's efficiency. The adopted NSGA-II optimization kernel is improved by applying the genetic operators of mutation and crossover on a transformed design-space, obtained from the latest set of solutions (the parents) using PCA. By applying crossover and mutation on variables that are projections of the principal components, the optimization moves more effectively, finding solutions with better performances, in the same amount of time, than the standard NSGA-II optimization kernel. The proposed method was validated in the optimization of two widely used analog circuits, an amplifier and a voltage controlled oscillator, reaching wider solutions sets, and in some cases, solutions sets that can be almost 3 times better in terms of hypervolume.

关键词： Electronic Design Automation Sizing optimization Analog and Radio-Frequency Integrated Circuits multi-objective evolutionary optimization Principal Component Analysis

来源：评论

学校读者我要写书评

暂无评论

Classification of Splice-Junction DNA Sequences Using multi-objective Genetic-Fuzzy optimization Techniques 16th

Classification of Splice-Junction DNA Sequences Using Multi-...

引用

16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lecia PP 7 PL-25314 Kielce Poland

ISBN: (纸本)9783319590639;9783319590622

The main goal of this paper is the application of our fuzzy rule-based classification technique with genetically optimized accuracy-interpretability trade-off to the classification of the splice-junction DNA sequences coming from the Molecular Biology (Splice-junction Gene Sequences) benchmark data set (available from the UCI repository). Two multi-objective evolutionary optimization algorithms are employed and compared in the framework of our technique, i.e., the well-known Strength Pareto evolutionary Algorithm 2 (SPEA2) and our SPEA2's generalization (referred to as SPEA3) characterized by a higher spread and a better-balanced distribution of solutions. A comparative analysis with 15 alternative approaches is also performed.

关键词： Splice-junction DNA sequence classification Fuzzy rule-based classifier multi-objective evolutionary optimization Accuracy-interpretability trade-off optimization

来源：评论

学校读者我要写书评

暂无评论

Racing multi-objective Selection Probabilities

Racing Multi-objective Selection Probabilities

引用

13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Marceau-Caron, Gaetan Schoenauer, Marc Thales Air Syst Rungis France Univ Paris 11 INRIA Saclay TAO Project Orsay France Univ Paris 11 LRI F-91405 Orsay France

ISBN: (纸本)9783319107622;9783319107615

In the context of Noisy multi-objective optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set. Approximating these statistics requires repeated samplings of the population, drastically increasing the overall computational cost. To tackle this issue, this paper proposes to directly estimate the probability of each individual to be selected, using some Hoeffding races to dynamically assign the estimation budget during the selection step. The proposed racing approach is validated against static budget approaches with NSGA-II on noisy versions of the ZDT benchmark functions.

关键词： multi-objective evolutionary optimization Hoeffding Races Uncertaintly Handling Noisy multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

ASMEvoNAS: Adaptive segmented multi-objective evolutionary network architecture search

引用

APPLIED SOFT COMPUTING 2023年 146卷

作者： Yan, Li Zhang, Zhipeng Liang, Jing Qu, Boyang Yu, Kunjie Wang, Kongyuan Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

Network architecture search (NAS) has attracted much attention as an automatic design technique of network architecture. In particular, multi-objective evolutionary algorithms (MOEAs) have been a popular kind of optimizer in NAS due to their global optimization capability. However, as a populationbased iterative search method, MOEAs are subject to the unbearable computational cost of individual evaluation on multiple objectives at each generation, which affects their generalization ability and transferability of MOEA-based NAS. Therefore, an adaptive segmented multi-objective evolutionary network architecture search (ASMEvoNAS) method is proposed in this paper. Firstly, an adaptive segmented evaluation strategy is designed to adaptively select different but more suitable objectives to efficiently assess the candidate architectures at different evolutionary stages, instead of evaluating them by all the considered objectives simultaneously. Thus, the computational cost and complexity of the search process can be controlled and reduced to some extent. Secondly, a preference-based pre-selection strategy is designed to filter out the initialized architectures with high parameter quantities to reduce the total parameter scale of the whole population and memory consumption. Last, a novel desirable gene reservation-based crossover and a directed connection-based mutation are proposed to produce offspring. Experimental results show that ASMEvoNAS shows promising performance on CIFAR-10, CIFAR-100, and ImageNet with error rates of 2.21%, 15.57%, and 24.43% top-1, respectively. The proposed method reduces the search cost to 0.36 GPU-Days on CIFAR-10 while maintaining competitive classification performance compared to state-of-the-art networks. In addition, ASMEvoNAS presents superior performance when dealing with the considered transfer tasks, as well as the benchmark dataset of NAS.

关键词： Convolutional neural networks Neural architecture search multi-objective evolutionary optimization Segmented evaluation

来源：评论

学校读者我要写书评

暂无评论

A Peer-to-Peer Energy Trading Model for Optimizing Both Efficiency and Fairness

引用

ENERGIES 2023年第14期16卷 5501页

作者： Kusatake, Eiichi Imahori, Mitsue Shinomiya, Norihiko Soka Univ Grad Sch Sci & Engn 1-236 Tangi Machi Hachioji 1928577 Japan

In recent years, there has been a growing global trend towards transitioning from centralized energy systems to distributed or decentralized models, with the aim of promoting the widespread utilization of renewable energy sources. As a result, the concept of direct energy trading among consumers has garnered considerable attention as a means to effectively harness the potential of distributed energy systems. However, in this decentralized trading scenario, certain consumers may encounter challenges in receiving electricity from their preferred suppliers due to limited supply capacities. As a result of this constraint, there is a reduction in the advantages enjoyed by consumers. While previous studies have predominantly focused on optimizing resource allocation efficiency, the issue of equitable consumer benefits has often been overlooked. Therefore, it is crucial to develop a trading mechanism that considers the preferences of market participants, in addition to balancing supply and demand. Such a mechanism aims to enhance both fairness and efficiency in the market. This paper introduces the formulation of a single-objective optimization and multi-objective optimization problem for an electricity market trading mechanism. To address this challenge, two single-objective algorithms and six evolutionary algorithms (EAs) are employed to solve the optimization problem. By analyzing the simulation results, this study demonstrates the efficacy of the chosen evolutionary algorithms (EAs) and a single-objective optimization approach in effectively optimizing both the utilization of resources and the equitable distribution of consumer benefits.

关键词： decentralized energy transactions multi-objective evolutionary optimization graph theory

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：