此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：Software Performance Optimization Based on Constrained GSA外文作者：Maryam Amoozegar，Hossein Nezamabadi-pour 文献出处: The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2018) (如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文3038单词，20341字符(字符就是印刷符)，中文4913汉字。（如果字数多了，可自行删减，大多数学校都是要求选取外文的一部分内容进行翻译的。）Software Performance Optimization Based on Constrained GSAAbstractSoftware Performance Engineering (SPE) in the early life cycle of software development (software modeling) is very useful and cost-effective but does not guide the software architect through how to improve the design. Computing the least response time by controlling utilization and cost is a constrained optimization problem. This paper presents a constrained optimization method based on Gravitational Search Algorithm (GSA) for exploring the software design space automatically and proposes the best configuration in terms of performance evaluation. Presented method is compared with constrained PSO which is one of famous optimization algorithms. Obtained results confirm the efficiency of proposed method.Keywords-component; Software performance engineering; Gravitational serach algorithm; constrained optimization;I.INTRODUCTIONSoftware Performance Engineering (SPE) 1 is the process of evaluating performance of software thorough the life cycle to increase performance. Research is done in component based software, indicates that the automation of SPE is very important because software designer can more easily create high-quality component-based software model. SPE process can be divided into 2 subprocesses; transformation and feedback. Transformation is a step for transforming software model into performance model, where feedback is done from performance model to software model for finding best configuration and design improvement on the software model. There has been various automation transformation approaches in the literature, especially when automatic finding of the best configuration has been a mature task in feedback subprocess 2. In software engineering, the most important performance factor is response time that must be minimized.Also utilization of software and hardware resources, throughput and cost must be controlled. Recently, several efforts have been performed to find and propose best configuration 3,4,5.These configuration are understandable for the software designer; therefore, software designer does not need to be expert in the performance evaluation field. Therefore, software performance optimization can be described as a constrained optimization problem. Objective is minimizing the response time whereas cost and utilization constraints have to be satisfied. In 6, the author has reviewed application of meta-heuristic algorithms in software engineering. These approaches explore designHossein Nezamabadi-pour Department of electrical Engineering, Shahid Bahonar University Kerman, Iran nezammail.uk.ac.ir space with meta-heuristic algorithms, for example genetic algorithm. Gravitational Search Algorithm (GSA) is a new heuristic optimization method that is based on the law of gravity and mass interactions 7. GSA has been applied for solving various nonlinear functions and compared with some well-know search methods. In this paper, GSA is applied to minimize response time. In order to consider cost and resources utilization constrained and bottlenecks controlling we present a constrained version of GSA. Proposed method applied in a case study and a constrained version of PSO8 also used to it. Obtained results confirm the efficiency of proposed method.II.RELATED WORKSExisting Classical performance analysis tools based on queueing network or stochastic Petri-nets ordinarily analysis model to produce performance metrics and do not provide feedback to improve the model. 2 Xu et al. presented a semi-automated rule-based approach in 3 that finds configuration and design improvement on the software model. In this work, the architecture model wasnt component based and heuristic rules were defined to increase the processing speed of bottleneck resources and thereby to help the software architect. Mostly, however, these approaches are notautomated. Additionally, the rules can only make use of the performance domain knowledge actually codified in these heuristics and cannot explore regions of the design space for which no prior knowledge exist. Martens in 4 presented a fully-automated approach to improve the expected performance of component-based software designs and a prototypical implementation for it. Exploration of the design space was done by genetic search techniques and performance-domain heuristics.Martens in 5 have presented another approach to optimize service-oriented architecture models with regard to performance and reliability. The approach is based on the Palladio Component Model