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Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms

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Nuclear Science and Techniques 2023年第10期34卷 20-35页

作者： He-Xing Yin Jia-Bao Guan Shun-Qiang Tian Ji-Ke Wang The Institute for Advanced Studies Wuhan UniversityWuhan 430072China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China

Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate when the optimization objectives for an accelerator are equal to or greater than four. Recently, many-objective evolutionary algorithms(MaOEAs)that can solve problems with four or more optimization objectives have received extensive attention. In this study, two diffraction-limited storage ring(DLSR) lattices of the Extremely Brilliant Source(ESRF-EBS) type with different energies were designed and optimized using three MaOEAs and a widely used MOEA. The initial population was found to have a significant impact on the performance of the algorithms and was carefully studied. The performances of the four algorithms were compared, and the results demonstrated that the grid-based evolutionary algorithm(grea) had the best *** OEAs were applied in many-objective optimization of DLSR lattices for the first time, and lattices with natural emittances of 116 and 23 pm·rad were obtained at energies of 2 and 6 GeV, respectively, both with reasonable dynamic aperture and local momentum aperture(LMA). This work provides a valuable reference for future many-objective optimization of DLSRs.

关键词： Storage ring lattices Many-objective evolutionary algorithms grea algorithm NSGA

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A hierarchical structure for optimal resource allocation in geographically distributed clouds

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 90卷 539-568页

作者： Ziafat, Hasan Babamir, Seyed Morteza Univ Kashan Dept Software Engn Kashan Iran

Due to the growth of users' requests for various resources in cloud computing, the optimal resource allocation is one of the most important challenges in cloud environments. The optimal resource allocation is achieved by considering user requirements stated in Service Level Agreements (SLAB) and the Quality of Services (QoSs) provided by resources. Since some user requirements (objectives) conflict with some others, a optimal trade-off between them is required in the selection of resources. Obtaining such a trade-off is a complicated and NP-hard problem because we may come up with a lot of permutations (choices) of the resources with the desired QoS. If a cloud environment is geographically distributed, the problem becomes more complicated because in the geographically distributed cloud there are a lot of candidate datacenters with qualified resources. The user requirements considered in this paper are availability and reliability of resources should be maximized and resource cost and response time should be minimized as well as the minimization of the network traffic. The maximization of and the minimization of the requirements conflict with each other;therefore a trade-off between them is required. In this paper, a hierarchical structure with two resource selections methods called Simplex Linear Programming (SLP) Method and grea-based method are used where the hierarchical structure is used to present connections between distributed datacenters and the methods are used to select optimal resources among the datacenters. The most important feature of the hierarchical structure is to prevent the occurrence of an accumulation of requests in a datacenter leading to increase the rate of finding optimal VMs. Moreover, in most studies on geographically distributed clouds, one user requirement is considered for the optimal selection of resources;however, in this study, datacenters are selected based on 4 user requirements as well as the network traffic. Moreover, while in

关键词： Geographically distributed clouds Many-objective algorithm VM allocation Linear programming (LP) grea algorithm Hierarchical structure

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