检索结果-内蒙古大学图书馆

multi-objective evolutionary optimization based on online perceiving Pareto front characteristics

INFORMATION SCIENCES 2021年 581卷 912-931页

作者： Feng, Wenqing Gong, Dunwei Yu, Zekuan China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ Huashan Hosp Ctr Shanghai Intelligent Imaging Crit Brain Dis E Shanghai 200433 Peoples R China

When solving multi-objective optimization problems with complex Pareto fronts characteristics, previous work generally ignores the information related to Pareto fronts provided by the population during the evolution, which is detrimental to efficiently tackle them. In order to taking full advantage of information associated with a population evolution, a multi-objective evolutionary optimization method based on online perceiving Pareto front characteristics is proposed in this study. To this end, the information associated with the Pareto front of an optimization problem is first extracted from the population. Following that, the characteristics of the Pareto front in concavity/convexity and continuity are perceived online. For the purpose of each sub-front containing only one characteristic, a Pareto front is divided based on the concavity/convexity and continuity. According to the characteristic of each sub-front, different reference points are selected to refine the distribution of reference vectors. Finally, a multi-objective evolutionary algorithm is designed targeting the characteristics of the Pareto front. The performance of the proposed method is evaluated by comparing it with 8 state-of-the-art optimizers on 31 test problems. Further, the experimental results demonstrate that the proposed method is competitive in handling multi-objective optimization problems with irregular Pareto fronts. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary optimization Irregular Pareto fronts Perception Continuity Convexity concavity

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary optimization of photovoltaic glass for thermal, daylight, and energy consideration

引用

SOLAR ENERGY 2023年 264卷

作者： Taser, Aybuke Kazanasmaz, Tugce Koyunbaba, Basak Kundakci Arsan, Zeynep Durmus Catholic Univ Louvain Louvain Res Inst Landscape Architecture & Climat ArchitectureBuilt Environm Louvain La Neuve Belgium Izmir Inst Technol Fac Architecture Dept Architecture Izmir Turkiye Yasar Univ Fac Architecture Dept Architecture Bornova Izmir Turkiye

The potential of fenestration systems is increased by incorporating photovoltaic technology into windows. This recently developed technology enhances the ability to generate energy from the building facade, improve the thermal and daylight performance of buildings, and visual comfort of occupants. Integrating an evolutionary optimization algorithm into this technology is one of the possible sustainable solutions to enhance building performance and minimize environmental impact. This paper uses a genetic evolutionary optimization algorithm to explore the optimum performance of photovoltaic glass in an architecture studio regarding annual energy consumption, energy generation, and daylight performance. Design variables include a window-to-wall ratio (i. e., window size and location) and amorphous-silicon thin-film solar cell transparency to generate optimum Pareto-front solutions for the case building. optimization objectives are minimizing annual thermal (i.e., heating and cooling) loads and maximizing Spatial Daylight Autonomy. Optimized results of low-E semi-transparent amorphous-silicon photovoltaic glass applied on the facade show that the spatial daylight autonomy is increased to 82% with reduced glare risk and higher visual comfort for the occupants. Photovoltaic glass helped reduce the selected room's seasonal and annual lighting loads by up to 26.7%. Lastly, compared to non-optimized photovoltaic glass, they provide 23.2% more annual electrical energy.

关键词： BIPV Daylight multi-objective evolutionary optimization Spatial daylight illuminance Thin-film photovoltaics

来源：评论

学校读者我要写书评

暂无评论

multi-objective evolutionary optimization of extreme gradient boosting regression models of the internal turning of PEEK tubes

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Vieira, Jessica Tito Pereira, Robson Bruno Dutra Lauro, Carlos Henrique Brandao, Lincoln Cardoso Ferreira, Joao Roberto Univ Fed Itajuba Inst Ind Engn & Management Itajuba MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Sustainable CIMS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn Sao Joao Del Rei MG Brazil

Internal turning or boring is an attractive machining process for hole enlarging as it presents low costs, good flexibility, and adaptability. The process presents some instability problems due to the high relation length/diameter ratio of the boring bar. To achieve the best process conditions, predictive models must be estimated and optimization must be conducted. This work presents a statistical learning approach for modeling and optimization of the internal turning process in PEEK tubes. Cutting speed, feed rate, and fixture position were considered input parameters. Cross-validation is used for learning and model selection, including k-fold and bootstrap approaches. The results pointed out that the extreme gradient boosting model was the best for all predictors. For R-a the final prediction metrics results were RMSE = 0.1395, MEA = 0.1126, and R-2 = 1.0031, for F-c, RMSE = 1.8609, MAE = 0.9311, and R-2 = 0.9280, and for Ron(t), RMSE = 21.3084, MAE = 17.8053, and R-2 = 0.6562. These results were much superior to the other methods. multi-objective evolutionary optimization was performed considering the extreme gradient boosting models for roughness, total roundness, and cutting force, besides the deterministic model of the material removal rate. The NSGA-II method was selected considering the hypervolume metric. The pseudo-weight approach is used for select high trade-off solutions to be used in practical production scenarios.

关键词： Internal turning Polyetheretherketone Statistical learning Machine learning Extreme gradient boosting multi-objective evolutionary optimization

来源：评论

学校读者我要写书评

暂无评论

Assessment of Penalty-Free multi-objective evolutionary optimization Approach for the Design and Rehabilitation of Water Distribution Systems

引用

WATER RESOURCES MANAGEMENT 2014年第2期28卷 373-389页

作者： Siew, Calvin Tanyimboh, Tiku T. Seyoum, Alemtsehay G. Univ Strathclyde Dept Civil & Environm Engn Glasgow G4 0NG Lanark Scotland

This paper describes a penalty-free multi-objective evolutionary optimization approach for the phased whole-life design and rehabilitation of water distribution systems. The optimization model considers the initial construction, rehabilitation and upgrading costs. Repairs and pipe failure costs are included. The model also takes into consideration the deterioration over time of both the structural integrity and hydraulic capacity of every pipe. The fitness of each solution is determined from the trade-off between its lifetime costs and its actual hydraulic properties. The hydraulic analysis approach used, known as pressure-dependent modelling, considers explicitly the pressure dependency of the water supply consumers receive. Results for two sample networks in the literature are included that show the algorithm is stable and finds optimal and near-optimal solutions reliably and efficiently. The results also suggest that the evolutionary sampling efficiency is very high. In other words, the number of solutions evolved and analysed on average before finding a near-optimal solution is small in comparison to the total number of feasible and infeasible solutions. We found better solutions than those reported previously in the literature for the two networks considered. For the Kadu network, for example, the new best solution costs Rs125,460,980-a significant improvement. Additional statistics that are based on extensive testing are included.

关键词： multi-objective evolutionary optimization Pressure-dependent analysis Water distribution system Genetic algorithm Hydraulic reliability Penalty-free boundary search

来源：评论

学校读者我要写书评

暂无评论

Application of multi-objective evolutionary optimization Algorithms to Reactive Power Planning Problem

引用

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2009年第5期4卷 625-632页

作者： Eghbal, Mehdi Yorino, Naoto Zoka, Yoshifumi El-Araby, E. E. Hiroshima Univ Grad Sch Engn Artificial Complex Syst Engn Dept Higashihiroshima 7398527 Japan Suez Canal Univ Dept Elect Engn Port Said Egypt

This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms (EAs). Specifically, strength Pareto EA (SPEA) and multi-objective particle swarm optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost of the VAr planning problem and maximizing the amount of available transfer capability (ATC) are defined as the main objective functions. The aim is to find the optimal allocation of VAr devices in such a way that investment and operating costs are minimized and at the same time the amount of ATC is maximized. The proposed approaches have been successfully tested on IEEE 14 buses system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large-scale multi-objective optimization problem. (C) 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： available transfer capability multi-objective evolutionary optimization multi-objective particle swarm optimization reactive power planning strength Pareto evolutionary algorithm voltage collapse

来源：评论

学校读者我要写书评

暂无评论

An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures

引用

EXPERT SYSTEMS WITH APPLICATIONS 2018年 102卷 262-272页

作者： Ho-Huu, V Duong-Gia, D. Vo-Duy, T. Le-Duc, T. Nguyen-Thoi, T. Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Vietnamese German Univ Fac Engn Binh Duong New City Vietnam

Over the past decades, the reliability-based design optimization of truss structures has been still a major challenge for engineering designers and even for researchers because of its complexities and high computational cost. In this paper, a new approach based on a novel combination of multi-objective evolutionary optimization and reliability analysis is proposed to deal with such kinds of problems. The proposed method consists of two separate steps. First, a multi-objective design optimization problem is formulated and solved by a multi-objective evolutionary optimization algorithm. Secondly, reliability analysis problems are formed by taking into account the uncertainty of input data of the problem, and a reliability analysis method is used to evaluate the reliability of all solutions obtained at the first step. Based on the obtained reliability, the suitable optimal solutions with a certain level of reliability can be readily identified. The proposed approach has some advantages such as: 1) it can give a set of many optimal solutions with different levels of reliability by only one run;2) it can easily handle the design optimization problems of truss structures with continuous, discrete or mixed continuous-discrete design variables;3) it is quite simple for engineering designers to understand and implement. To demonstrate the efficiency and applicability of the proposed method, three examples of truss structures are carried out, and the obtained results are compared to those available in the literature. The acquired results reveal that the proposed approach is reliable and more competitive compared to other methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary optimization Reliability-based design optimization (RBDO) Structural optimization Non-dominated sorting genetic algorithm II (NSGA-II) First order reliability analysis (FORM) Truss structures

来源：评论

学校读者我要写书评

暂无评论

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

引用

ENVIRONMENTAL MODELLING & SOFTWARE 2015年 69卷 262-279页

作者： Yang, Tiantian Gao, Xiaogang Sellars, Scott Lee Sorooshian, Soroosh Univ Calif Irvine Dept Civil & Environm Engn Irvine CA 92617 USA

This study demonstrates the application of an improved evolutionary optimization Algorithm (EA), titled multi-objective Complex Evolution Global optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original multi-objective Complex Evolution Global optimization Method (MOCOM), the multi-objective Differential Evolution method (MODE), the multi-objective Genetic Algorithm (MOGA), the multi-objective Simulated Annealing approach (MOSA), and the multi-objective Particle Swarm optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficie

关键词： multi-objective evolutionary optimization Decision making Oroville-Thermalito complex Reservoir operation

来源：评论

学校读者我要写书评

暂无评论

Handling fuzzy systems' accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods - selected problems

引用

BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2015年第3期63卷 791-798页

作者： Gorzalczany, M. B. Rudzinski, F. Kielce Univ Technol Dept Elect & Comp Engn PL-25314 Kielce Poland

The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs' accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).

关键词： accuracy and interpretability of fuzzy rule-based systems multi-objective evolutionary optimization genetic computations fuzzy systems

来源：评论

学校读者我要写书评

暂无评论

A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2013年第10期40卷 3886-3899页

作者： Grasso, Raffaele Cococcioni, Marco Mourre, Baptiste Osler, John Chiggiato, Jacopo CMRE I-19126 La Spezia Italy Univ Pisa Dipartimento Ingn Informaz I-56123 Pisa Italy Ist Sci Marine CNR ISMAR Consiglio Nazl Ric I-30122 Venice Italy

A decision support system for the optimal deployment of drifting acoustic sensor networks for cooperative track detection in underwater surveillance applications is proposed and tested on a simulated scenario. The system integrates sea water current forecasts, sensor range models and simple drifting buoy kinematic models to predict sensor positions and temporal network performance. A multi-objective genetic optimization algorithm is used for searching a set of Pareto optimal deployment solutions (i.e. the initial position of drifting sonobuoys of the network) by simultaneously optimizing two quality of service metrics: the temporal mean of the network area coverage and the tracking coverage. The solutions found after optimization, which represent different efficient tradeoffs between the two metrics, can be conveniently evaluated by the mission planner in order to choose the solution with the desired compromise between the two conflicting objectives. Sensitivity analysis through the Unscented Transform is also performed in order to test the robustness of the solutions with respect to network parameters and environmental uncertainty. Results on a simulated scenario making use of real probabilistic sea water current forecasts are provided showing the effectiveness of the proposed approach. Future work is envisioned to make the tool fully operational and ready to use in real scenarios. (C) 2013 NATO Science and Technology Organization, Centre for Maritime Research and Experimentation. Published by Elsevier Ltd. All rights reserved.

关键词： Dynamic sensor networks Optimal deployment multi-objective evolutionary optimization Decision support systems Ensemble forecasts Surveillance

来源：评论

学校读者我要写书评

暂无评论

A multi-objective evolutionary optimization framework for a natural gas liquids recovery unit

引用

COMPUTERS & CHEMICAL ENGINEERING 2021年 151卷 107363-107363页

作者： Salas, Santiago D. Contreras-Salas, Lizbeth Rubio-Duenas, Pamela Chebeir, Jorge Romagnoli, Jose A. ESPOL Escuela Super Politecn Litoral Fac Ciencias Nat & Matemat Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Louisiana State Univ Dept Chem Engn Baton Rouge LA 70803 USA

A simulation-based multi-objective optimization scheme is proposed for determining the optimal operating conditions of a natural gas liquids (NGL) recovery unit. Two objective functions are considered, the annualized profitability of the unit and the concentration of methane in the NGL product stream. Two problem formulations are studied including a deterministic model and a stochastic model which incorporates market uncertainty. The techno-economic framework combines the process simulation package PRO/II and a Python environment, in which the simulation status is tracked through the optimization. An evolutionary optimization algorithm simultaneously optimizes eight decision variables for constructing a 2-D Pareto front. Results provide insightful guidance on determining the most adequate conditions of a gas subcooled process (GSP) unit and portray an operational back-off which aims to reduce the impact introduced by market uncertainties. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary optimization Gas subcooled process Market uncertainty Operational back-off

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：