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Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms 作者： Yin, Hexing 1 Guan, Jiabao 1 Tian, Shunqiang 2 Wang, Jike 1 作者单位： 1. The Institute for Advanced Studies, Wuhan University, Wuhan 430072, China 2. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China 通讯作者： Wang, Jike Email:[email protected] 提交时间： 2023-08-11 摘要: 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 [A1]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 ESRF-EBS [A2]type with different energies were designed and optimized using three MaOEAs and a widely used MOEA. The initial population[A3] 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 performance. MaOEAs were applied in many-objective optimization of DLSR lattices for the first time, and lattices with natural emittances of 116 pm∙rad and 23 pm∙rad were obtained at energies of 2 GeV and 6 GeV, respectively, both with reasonable dynamic aperture and local momentum aperture (LMA). This work provides a valuable reference for future multi-objective optimization of DLSRs. storage ring lattices many-objective evolutionary algorithms GrEA algorithm NSGA 来自： Jia-Bao Guan 分类： 物理学 >> 核物理学 投稿状态： 未投稿 引用： ChinaXiv:202308.00195 (或此版本 ChinaXiv:202308.00195V1 ) doi:10.12074/202308.00195 CSTR:32003.36.ChinaXiv.202308.00195.V1 推荐引用方式： Yin, Hexing ,Guan, Jiabao ,Tian, Shunqiang ,Wang, Jike .(2023).Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms.中国科学院科技论文预发布平台.[ChinaXiv:202308.00195] 版本历史 [V1] 2023-08-11 16:51:06 ChinaXiv:202308.00195V1 下载全文

Evolutionary algorithms are popular heuristics for solving various combinatorial problems as they are easy to apply and often produce good results. Island models parallelize evolution by using different populations, called islands, which are connected by a graph structure as communication topology. Each island periodically communicates copies of good solutions to neighboring islands in a process called migration. We consider the speedup gained by island models in terms of the parallel running time for problems from combinatorial optimization: sorting (as maximization of sortedness), shortest paths, and Eulerian cycles. Different search operators are considered. The results show in which settings and up to what degree evolutionary algorithms can be parallelized efficiently. Along the way, we also investigate how island models deal with plateaus. In particular, we show that natural settings lead to exponential vs. logarithmic speedups, depending on the frequency of migration.

Derek Kozel - Optimization of Digital Modulation Schemes using Evolutionary Algorithms Source: https://www.youtube.com/watch?v=rHEnt9ZiS6o Uploader: GNU Radio

Software testing is very time consuming, labor-intensive and complex process. It is found that 50% of the resources of the software development are consumed for testing. Testing can be done in two different ways such as manual testing and automatic testing. Automatic testing can overcomes the limitations of manual testing by decreasing the cost and time of testing process. Path testing is the strongest coverage criteria among all white box testing techniques as it can detect about 65% of defects present in a SUT. With the help of path testing, the test cases are created and executed for all possible paths which results in 100% statement coverage and 100% branch coverage. This paper presents a systematic review of test data generation and optimization for path testing using Evolutionary Algorithms (EAs). Different EAs like GA, PSO, ACO, and ABCO based methods has been already proposed for automatic test case generation and optimization to achieve maximum path coverage. 

Many researchers have applied single-objective optimization algorithms for structural damage identification. Recently, multi-objective optimization algorithms have also been considered for this purpose. This article applies two recent multi-objective evolutionary optimization algorithms and modal shape and stiffness objective functions for structural damage detection in a simply supported beam and a two fixed-end moment beam. For the fixed supported beam, the results are compared with the experimental results from the literature. The results demonstrate the good performance of proposed approach in localization and quantification of damages.

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this paper, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the $k$-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

Different structures of microstrip antenna optimization using different algorithms are the important field of wireless communications. Rectangular microstrip antenna, inverted E-shaped antenna, tulip shaped antenna are some examples of microstrip antennas. The antenna dimensions are optimized by different algorithms. The operating frequencies for different antenna structures depend on antenna dimensions. The frequency of operation is 3-lS GHz for rectangular antenna, IMT-2000 for invelted E-shaped antenna, 8 to 12 GHz for tulip shaped antenna, 2.16 GHz for miniaturized antenna structure. The dimensions of microstrip antennas are modified to get minimum reflection coefficient maximum gain and bandwidth. The dimensions are modified using different algorithms such as evolutionary algorithm, particle swarm optimization (PSO), artificial neural network (ANN), and genetic algorithms (GA).

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an arti cial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus ac

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an arti cial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus ac

1Christmas Stories from French and Spanish Writers

By Pedro Antonio de Alarcón y Ariza, Gustavo Adolfo Bécquer, François Coppée, Alphonse Daudet, Antoine Gustave Droz, Maxime Du Camp, Antoinette Ogden, Benito Pérez Galdós, Marcel Prévost, Jules Simon, André Theuriet and Armando Palacio Valdés

Fifteen short stories by Antoinette Ogden from French and Spanish writers of many times. - Summary by david wales

Professor H. L. Sabsovich is recognized as one of the foremost figures in the field of Jewish social service. A great part of his active life was given to the work of the Baron de Hirsch Fund, and he distinguished himself as a visionary thinker as well as an excellent administrator and, significantly, the Builder of Woodbine, the Woodbine Agricultural School, and later as General Agent for the Baron de Hirsch Fund.