检索结果-内蒙古大学图书馆

Intensifying the search-based optimization of product line architectures with crossover operators

EMPIRICAL SOFTWARE ENGINEERING 2022年第7期27卷 166-166页

作者： da Silva, Diego Fernandes Okada, Luiz Fernando Assuncao, Wesley K. G. Colanzi, Thelma Elita State Univ Maringa UEM Maringa Parana Brazil DI Pontifical Catholic Univ Rio de Janeiro PUC Ri Rio De Janeiro Brazil PPGComp Western Parana State Univ Unioeste Cascavel Brazil

The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results.

关键词： multi-objective evolutionary algorithm Software product line Software architecture Recombination operators

来源：评论

学校读者我要写书评

暂无评论

multi-objective Sparse Reconstruction With Transfer Learning and Localized Regularization

引用

IEEE ACCESS 2020年 8卷 184920-184933页

作者： Yan, Bai Zhao, Qi Zhang, J. Andrew Wang, Zhihai Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Technol Sydney Global Big Data Technol Ctr GBDTC Ultimo NSW 2007 Australia Beijing Univ Technol Minist Educ Key Lab Optoelect Technol Beijing 100124 Peoples R China

multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.

关键词： Search problems Image reconstruction Knowledge transfer Iterative methods Convergence evolutionary computation Optimization Sparse reconstruction multi-objective evolutionary algorithm transfer learning regularization

来源：评论

学校读者我要写书评

暂无评论

Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Zhao, Ziyan Jiang, Qi Liu, Shixin Zhou, Mengchu Yang, Xiaochun Guo, Xiwang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China

Energy consumption, production cost, and efficiency are highly concerned by decision makers of energyintensive and high -cost industrial production systems. Intelligent production scheduling is a necessary means to achieve their optimization. This work delves into a novel multi -objective production scheduling problem arising from a steel hot -rolling process, which is a representative energy -intensive and high -cost industrial process. The challenge of the problem involves scheduling customized production jobs subject to intricate process constraints with the goal to minimize three objective functions, i.e., energy consumption, setup cost, and the number of tardy jobs. A mixed integer linear programming model is formulated for the problem. In order to solve it, an improved multi -objective evolutionary algorithm based on decomposition is presented. The algorithm incorporates problem -specific encoding and model -based decoding mechanisms, rendering it wellsuited for addressing the concerned multi -constrained multi -objective optimization problem. The introduced modified Tchebycheff approach mitigates the impact of objective functions with varying value ranges on the algorithm's convergence. Additionally, a Metropolis acceptance criterion is integrated to facilitate the escape from local optimal solutions, enhancing the algorithm's global optimization capability. Numerous experiments are conducted to verify the effectiveness of the improvements and to compare the performance of the presented algorithm against its competitive peers. The results demonstrate its high performance, suggesting its significant potential for its application to steel hot -rolling systems.

关键词： Green manufacturing Mixed integer linear program multi-objective evolutionary algorithm Production scheduling

来源：评论

学校读者我要写书评

暂无评论

Space division and adaptive selection strategy based differential evolution algorithm for multi-objective satellite range scheduling problem

引用

SWARM AND evolutionary COMPUTATION 2023年 83卷

作者： Wang, Tianyu Luo, Qizhang Zhou, Ling Wu, Guohua Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

Satellite range scheduling always plays a crucial role in tracking, telemetry, and control of the spacecraft. With the significant increase in the number and type of satellites in orbit, the demands of users for satellite range scheduling are becoming more and more diverse. However, existing studies for the satellite range scheduling problem (SRSP) rarely consider multiple optimization objectives, which is quite significant in practice. Hence, this paper investigates a multi-objective satellite range scheduling problem (MO-SRSP) that optimizes three objectives simultaneously: the overall profits of tasks, load-balance of antennas, and completion timeliness of tasks. To address MO-SRSP, this paper establishes a mathematical model on the basis of analysis of MO-SRSP and proposes a multi-objective evolutionary algorithm, which is called multi-objective differential evolution algorithm based on space division and adaptive selection strategy (MODE-SDAS). The space division strategy will uniformly divide the objective space into a set of subspaces and preserve a set of non-dominated solutions in each subspace during the environmental selection even if some of these solutions are dominated by other solutions in other subspaces, so as to maintain the diversity of the algorithm. The adaptive selection strategy will adaptively allocate computational resources to different subspaces to improve the convergence of the population. Besides, problem-specific designs such as coding and encoding methods, the discrete differential evolution operator, as well as objective-specific individual variation operators are incorporated into the algorithm to enhance the search capability. Finally, extensive experiments based on simulation test cases are carried out to verify the effectiveness and efficiency of MODE-SDAS. The comparison results show that MODE-SDAS significantly outperforms its competitors in terms of three metrics. Meanwhile, knee point analysis, sensitivity analysis, and applic

关键词： Satellite range scheduling problem multi-objective evolutionary algorithm Differential evolution Space division strategy Adaptive strategy

来源：评论

学校读者我要写书评

暂无评论

Neuroevolutionary diversity policy search for multi-objective reinforcement learning

引用

INFORMATION SCIENCES 2024年 657卷

作者： Zhou, Dan Du, Jiqing Arai, Sachiyo Chiba Univ Grad Sch Sci & Engn Dept Urban Environm Syst Div Earth & Environm Sci Chiba Japan

Sequential decision-making requires balancing multiple conflicting objectives through multi objective reinforcement learning (MORL). Moreover, decision-makers desire dense solutions that satisfy their requirements and consider the trade-offs between different objectives (Pareto optimal solutions). Most deep reinforcement learning methods focus on single-objective problems or solve multi-objective problems using simple linear combinations, which may oversimplify the underlying problem and lead to suboptimal results. This study proposes a neuroevolutionary diversity policy search approach to address MORL problems. It employs neural networks, each equipped with a buffer for storing recent experiences, representing individuals in a population. The non-dominated sorting method and diversity distance metric are employed in the evolutionary process to select high-quality solutions as teachers. The teachers use gradient-based genetic operators to guide the population to produce high-quality offspring, thereby achieving dense Pareto optimal solutions. Furthermore, we introduce three MORL benchmarks with distinct characteristics: (1) a continuous deep sea treasure with convex and nonconvex Pareto fronts;(2) a multi-objective mountain car with sparse rewards and a discontinuous Pareto front;and (3) a multi-objective HalfCheetah with high-dimensional action-state spaces. The experimental results on the three MORL benchmarks demonstrate the superiority of the proposed algorithm in obtaining dense and high-quality Pareto optimal solutions.

关键词： multi-objective reinforcement learning Pareto front Policy search multi-objective evolutionary algorithm Diversity

来源：评论

学校读者我要写书评

暂无评论

multi-objective optimization of a dimpled channel for heat transfer augmentation

引用

HEAT AND MASS TRANSFER 2008年第2期45卷 207-217页

作者： Samad, Abdus Lee, Ki-Don Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

Staggered arrays of dimples printed on opposite surfaces of a cooling channel is formulated numerically and optimized with hybrid multi-objective evolutionary algorithm and Pareto optimal front. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing, and dimple depth, to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-means clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

关键词： FLOW STRUCTURE NUMERICAL PREDICTIONS SHAPE OPTIMIZATION SURFACES FRICTION NUMBER DEPTH WALL multi-objective evolutionary algorithm shape optimization wall Heat exchange intensification dimple design variable Optimal design friction

来源：评论

学校读者我要写书评

暂无评论

A multi-objective optimization approach in defining the decarbonization strategy of a refinery

引用

SMART ENERGY 2022年 6卷

作者： de Maigret, Jacopo Viesi, Diego Mahbub, Md Shahriar Testi, Matteo Cuonzo, Michele Thellufsen, Jakob Zinck Ostergaard, Poul Alberg Lund, Henrik Baratieri, Marco Crema, Luigi Fdn Bruno Kessler FBK Via Sommar 18 I-38123 Povo TN Italy Ahsanullah Univ Sci & Technol AUST Love Rd 141&142 Tejgaon Ind Area Dhaka 1208 Bangladesh Sonatrach Raffineria Italiana Srl Raffineria Augusta I-96011 Augusta Contrada Marcel Italy Aalborg Univ Rendsburggade 14 DK-9000 Aalborg Denmark Univ Bozen Bolzano Fac Sci & Technol Univ pl 5 Piazza Univ5 I-39100 Bolzano BZ Italy

Nowadays, nearly one quarter of global carbon dioxide emissions are attributable to energy use in in-dustry, making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a multi -objective evolutionary algorithm (MOEA), considering the minimization of annual cost and CO2 emis-sions as two potentially conflicting objectives and the energy technologies' capacities as decision vari-ables. For the target year 2025, EnergyPLAN + MOEA has allowed to model a range of 0e100% decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical, thermal, hydrogen feedstock and transport demand. A set of nine scenarios, with different land use availabilities and implementable technologies, each consisting of 100 optimal systems out of 10,000 simulated ones, has been evaluated. The results show, on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones, on the other, how the decar-bonization pathways strongly depend on the available land for solar thermal, photovoltaic and wind, as well as the presence of a biomass supply chain in the region.& COPY;2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Refining industry Energy system integration Renewable energy Low-carbon economy multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Impact of selection methods on the diversity of many-objective Pareto set approximations

引用

Procedia Computer Science 2017年 112卷 844-853页

作者： Luis Martí Eduardo Segredo Nayat S´nchez-Pi Emma Hart Institute of Computing Universidade Federal Fluminense Niteroi (RJ) Brazil TAU team CNRS/INRIA/LRI Universite Paris-Saclay Paris France School of Computing Edinburgh Napier University Edinburgh Scotland UK Dpto. Ingeniería Informática y de Sistemas Universidad de La Laguna San Cristobal de La Laguna Spain DICC/IME Rio de Janeiro State University Rio de Janeiro (RJ) Brazil

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator-based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results.

关键词： multi-objective evolutionary algorithm many-objective optimisation selection mechanism diversity preservation

来源：评论

学校读者我要写书评

暂无评论

Optimization of subsurface models with multiple criteria using Lexicase Selection

引用

OPERATIONS RESEARCH PERSPECTIVES 2022年 9卷

作者： He, Yifan Aranha, Claus Hallam, Antony Chassagne, Romain Univ Tsukuba Sch Syst & Informat Engn Tsukuba Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Japan Heriot Watt Univ Energy Geosci Infrastructure & Soc Edinburgh Scotland

Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with multi-objective evolutionary algorithms (MOEAs). This group of algorithms optimize multiple objectives simultaneously, considering the trade-off between objectives. However, SHM requires the solutions that are good on all objectives rather than a trade-off. In this study, we propose a Differential Evolution algorithm using Lexicase Selection to solve the SHM problems. Unlike the MOEAs, this selection method pushes the solutions to perform well on all objectives. We compared this method with two MOEAs, namely Non-dominated Sorting Genetic algorithm II and Reference Vector-guided evolutionary algorithm, on two SHM problems. The results show that this method generates more solutions near the ground truth.

关键词： Seismic History Matching Lexicase Selection multi-objective optimization multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

multi-objective Evolution of Machine Learning Workflows

Multi-Objective Evolution of Machine Learning Workflows

引用

IEEE Symposium Series on Computational Intelligence

作者： Tomas Kren Martin Pilat Roman Neruda Faculty of Mathematics and Physics Charles university Prague Czech republic Institute of Computer Science Czech Academy of Sciences Prague Czech republic

In this paper we describe a multi-objective genetic programming algorithm which can be used to create complete machine learning workflows. The algorithm is an extension of a single-objective one. In a series of test on four datasets, we show that the additional objectives can be used to search for smaller or faster models. The algorithm is also in some cases much faster than the single-objective one while obtaining results of similar quality.

关键词： Genetic programming multi-objective evolutionary algorithm Machine learning

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：