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机械工程学报 2013年第23期49卷 150-160页

作者：车林仙何兵程志红重庆工程职业技术学院机械工程学院重庆400037 中国矿业大学机电工程学院徐州221116 泸州职业技术学院工程机械智能优化设计泸州市重点实验室泸州646005

工程实践中存在大量约束多目标优化问题(Constrained multi-objective optimization problems,CMOPs),多目标进化算法是求解这类问题的一类有效方法。引入扇形采样技术,将二次变异双种群差分进化算法和约束处理方法相结合,设计求解CMOP... 详细信息

工程实践中存在大量约束多目标优化问题(Constrained multi-objective optimization problems,CMOPs),多目标进化算法是求解这类问题的一类有效方法。引入扇形采样技术,将二次变异双种群差分进化算法和约束处理方法相结合,设计求解CMOPs的进化算法——基于扇形采样的约束多目标差分进化算法(Sector-sampling-based constrained multi-objective differential evolution algorithm,SS-CMODE)。扇形采样可避免耗时的非劣操作,且能保证Pareto最优解集的良好逼近性和多样性。通过3个典型CMOPs的对比测试,表明SS-CMODE的解集均匀性和计算效率明显优于对比算法。以J23-80机械压力机使用的双曲柄串联机构多目标优化为例,研究新算法求解工程问题的有效性。以锻冲工作阶段平均速度波动最小和力传动性能最优为目标,建立机构的约束多目标优化模型,再应用SS-CMODE求解该问题。结果表明,该算法能求出多组满足约束条件的Pareto最优解,且解集均匀性良好。

关键词：多目标优化差分进化算法扇形采样 Pareto最优化机械压力机

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A COST-BENEFIT optimization MODEL FOR MAINTENANCE AND REHABILITATION ACTIVITIES OF ROADS

A COST-BENEFIT OPTIMIZATION MODEL FOR MAINTENANCE AND REHABI...

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作者： BHAGYASHREE PANDA National Institute of Technology Rourkela

学位级别：硕士

The maintenance and rehabilitation of existing, mature road facilities are becoming gradually more significant and important components of highway activity. An efficient maintenance and rehabilitation policy is essential for a safe, comfortable and cost-effective transportation system. But, decisions to maintain existing road facilities involve a number of possible options in activities ranging from routine maintenance to rehabilitation or reconstruction, in choices for allocation of resources throughout a highway network, and to decide between investments versus non-investment option. Moreover, any economic analysis should not only consider the cost factor but also be designed to give maximum coverage of benefits like changes in road maintenance costs, changes in accident rates, increased travel or demand, environmental effects, change in value of goods moved, changes in agricultural output, changes in services, changes in industrial output, changes in land values, etc. As a result of these characteristics developing a maintenance and rehabilitation policy for roads is difficult and new concepts and analytic approaches needs to be introduced to address this problem. optimization models is one such analytical approach which helps in making a cost-benefit analysis of maintenance and rehabilitation activities of roads and in comparing the various possible alternatives to give out the best activity within the budget allocated, before being actually carried out in practical. In the present study it was aimed to formulate a multi-objective optimization model considering all necessary as well as sufficient factors responsible in 'maintenance and rehabilitation activities' of highway facilities so as to minimize the total cost and increase the total return in terms benefits from improved road condition subject to the practical limitations faced by concerned agency and user due to deterioration in road condition. A non-dominated sorting genetic algorithm II (NSGA-II) based

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A Strategy to Solve MaOPs with multiple Swarms Based on Broadcast Communication

A Strategy to Solve MaOPs with Multiple Swarms Based on Broa...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Arion de Campos Aurora T.R. Pozo Elias P. Duarte Department of Informatics State University of Ponta Grossa Ponta Grossa Parana Brazil Department of Informatics Federal University of Parana Curitiba Brazil

The application of multi-objective evolutionary algorithms to solve many-objective optimization problems face several problems, most derived from the fact that there is no single best solution, but a set of solutions. To obtain this set of solutions, the Pareto Optimality Theory is often used. It is difficult for an algorithm to converge to the Pareto Front and at the same time to guarantee the diversity of the obtained solutions. A practical approach to deal with these issues is to employ multi-swarm strategies. multi-swarm strategies have already been proven to be good approaches to solve mono-objectives problems and in this work we propose a multi-swarm strategy to solve many-objective problems. We designed a PSO strategy based on independent swarms that interact by means of particle migration policies implemented with asynchronous broadcast communication. We empirically evaluated the performance of the proposed strategy, in particular the convergence and diversity of the obtained solutions, as well as the scalability with respect to the number of objectives. To the best of our knowledge this is the first work that evaluates the application of PSO based on multiple swarms to solve problems with a large number of objectives. Performance metrics were employed as quality indicators and results show that the multi-swarm execution does bring advantages to the convergence and diversity.

关键词： optimization Measurement Benchmark testing Convergence Particle swarm optimization Sociology Statistics

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A multi-objective Genetic Graph-Based Clustering algorithm with memory optimization

A Multi-Objective Genetic Graph-Based Clustering algorithm w...

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Congress on Evolutionary Computation

作者： Héctor D. Menéndez David F. Barrero David Camacho Computer Science Department Universidad Autónoma de Madrid Spain Departamento de Automática Universidad de Alcalá Spain

ISBN: (纸本)9781479904532

Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called multi-objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a multi-objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.

关键词： Clustering algorithms Sociology Statistics Measurement Algorithm design and analysis Genetics Genetic algorithms

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Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

Provisioning of requests for virtual machine sets with place...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Lei Shi Bernard Butler Dmitri Botvich Brendan Jennings Telecommunications Software & Systems Group (TSSG) Waterford Institute of Technology Ireland

We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.

关键词： Heuristic algorithms Vectors Security Resource management Runtime optimization Software

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Hybrid evolutionary optimal MEMS design

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期63卷 305-317页

作者： Zhang, Ying Agogino, Alice M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

A hybrid evolutionary design synthesis and optimization process for microelectromechanical systems (MEMS) devices has been developed. The process integrates a MEMS design component library with multiple simulation modules and two levels of design optimization: global multi-objective genetic algorithms (MOGA) and local gradient-based refinement. During the hybrid evolutionary design process, MOGA randomly searches the design space and approaches the desirable design solutions using probabilistic transition rules, and gradient-based local optimization refines promising design candidates with computational efficiency. To efficiently apply hybrid evolutionary optimization techniques on MEMS designs, a hierarchical tree-structured component-based genotype representation has been developed, which incorporates specific engineering knowledge into the design synthesis and optimization process. The MEMS design component library serves as a source of practical and efficient genotypes for the evolutionary process, with each component associated with its instructions and restrictions on genetic operations. The component-based genotype incorporated with engineering knowledge constrains evolutionary searching in appropriate and promising regions of the search space, allowing a deeper search in a given amount of time. Hybrid evolutionary MEMS design synthesis and optimization are demonstrated with surface-micromachined resonator and accelerometer designs.

关键词： Hybrid evolutionary process multi-objective genetic algorithm Microelectromechanical systems Design synthesis and optimization

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A design of a simple evolutionary multi-objective optimization method based on the combined use of scalarization and particle swarm optimization

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2012年第1期132卷 42-52+4页

作者： Masuda, Kazuaki Harada, Kazuaki Kurihara, Kenzo Kanagawa University 3-27-1 Rokkakubashi Kanagawa-ku Yokohama Kanagawa 221-8686 Japan

This paper proposes a general methodology of multi-objective optimization based on the combined use of scalarization and evolutionary computation approaches. Mathematically, it is guaranteed that a Pareto optimal solution of a multi-objective optimization problem (MOP) can be found by minimizing the corresponding augmented Tchebysheff scalarized function. In this way, different Pareto optimal solutions can be obtained by solving different single-objective optimization problems (SOPs) with different weight for scalarization. Aiming at knowing the global structure of Pareto optimal set and/or Pareto frontier of a MOP with simple computation, we propose the basic concept that, (a) the SOPs with different weight should be solved independently by some evolutionary computation algorithms;and, (b) previously obtained useful solutions should be partly reused for the optimization of another SOP. In particular, for continuous-variable MOPs, we also propose a practical computation method based on the concept, which uses Particle Swarm optimization (PSO) with effective reinitialization mechanism. The usefulness of the proposed methodology is demonstrated through numerical experiments with the proposed computation method. © 2012 The Institute of Electrical Engineers of Japan.

关键词： multiobjective optimization

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multi-objective optimization of Fusegates System under Hydrologic Uncertainties

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WATER RESOURCES MANAGEMENT 2012年第8期26卷 2323-2345页

作者： Takbiri, Zeinab Afshar, Abbas Univ Minnesota Dept Civil Engn St Anthony Falls Lab Minneapolis MN 55455 USA Univ Minnesota Natl Ctr Earth Surface Dynam Minneapolis MN USA Iran Univ Sci & Technol Dept Civil Engn Tehran Iran

Fusegates are independent units held only by the gravity installed on the free spillway of existing dams in order to increase reservoir storage and/or discharge capacity. Increasing reservoir storage in many dams can partly sacrifice dam's reliabilities. So considering the failure risk of a dam together with the amount of increase in the reservoir capacity can prevent selecting fusegates which seriously endanger a dam safety. However, Lack of accurate information on various damage functions and difficulty in quantifying failure consequences are among principal limitations that hinder practical application of conventional approaches which account failure risk in real world hydrosystems problems. This study develops two effective multi-objective frameworks to optimize fusegates' configuration in order to eliminate the need for such hard-to-get mathematical damage functions and provide valuable information on the failure risk, total cost, and increased water volume of a reservoir. The proposed models find trade-off solutions between two sets of conflicting objectives. The first competing objectives is investment cost and water storage and the second conflicting goals are water storage and failure probability under the inherent and parameter hydrologic uncertainties. Complicated flood routing phenomenon within a reservoir equipped with fusegates is explicitly taken into account to attain a more cost-effective and reliable design without jeopardizing the dam safety. Applicability and performance of the developed optimization schemes are discussed and demonstrated on a real life case study. The multi-objective optimization results represented as an ensemble of diverse trade-off solutions provide decision makers with more insight and understanding of system behavior and different design alternatives.

关键词： Fusegates Risk-based design multi-objective optimization Hydrologic uncertainties Dam safety

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右上腹肿瘤联合脏器切除手术入路及手术技巧探讨

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中国实用外科杂志 2013年第4期33卷 331-333页

作者：张健韩广森蒋志强郑州大学附属肿瘤医院普外科河南郑州450003

目的探讨右上腹肿瘤联合脏器切除的手术入路及手术技巧的安全性和可行性。方法回顾2004年1月至2012年2月期间,郑州大学附属肿瘤医院普外科收治的21例右上腹肿瘤行联合脏器切除病人的临床资料。采用右下腹膜后逆行入路或(和)肝被膜下入路... 详细信息

目的探讨右上腹肿瘤联合脏器切除的手术入路及手术技巧的安全性和可行性。方法回顾2004年1月至2012年2月期间,郑州大学附属肿瘤医院普外科收治的21例右上腹肿瘤行联合脏器切除病人的临床资料。采用右下腹膜后逆行入路或(和)肝被膜下入路,分析术中出血量、手术时间及术后并发症情况。结果本组21例病人均行右上腹肿瘤联合脏器切除术,采用右下腹膜后逆行入路的病人17例,采用肝被膜下入路联合右下腹膜后逆行入路的病人4例,平均出血量为350mL,平均手术时间105min,其中18例术后恢复顺利,1例出现胰瘘,1例出现胆瘘,1例出现粘连性肠梗阻,经通畅引流及营养支持治疗后均痊愈出院,无一例死亡。结论右上腹肿瘤联合脏器切除采用右下腹膜后逆行入路和(或)肝被膜下入路是安全、可行的。

关键词：右上腹联合脏器切除右下腹膜后逆行入路肝被膜下入路

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Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2012年第6期37卷 5385-5405页

作者： Sabio, N. Kostin, A. Guillen-Gosalbez, G. Jimenez, L. Univ Rovira & Virgili URV Dept Engn Quim EQ Escola Tecn Super Engn Quim ETSEQ Tarragona 43007 Spain

Assessing the environmental performance of hydrogen infrastructures is essential for determining their practical viability. Previous optimization approaches for hydrogen networks have focused on optimizing a single environmental metric in conjunction with the economic performance. This approach is inadequate as it may leave relevant environmental criteria out of the analysis. We propose herein a novel framework for optimizing hydrogen supply chains (SC) according to several environmental indicators. Our method comprises two steps. In step one, we formulate a multi-objective mixed-integer linear program (MILP) that accounts for the simultaneous minimization of the most relevant life cycle assessment (LCA) impacts. Principal Component Analysis (PCA) is next employed in the post-optimal analysis of the MILP in order to facilitate the interpretation and analysis of its solution space. We demonstrate the capabilities of this approach through its application to the design of the future (potential) hydrogen SC in Spain. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen supply chain multi-objective optimization Mixed-integer linear programming Principal component analysis Life cycle assessment

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