Rule-based software architecture layer performance evolution optimizing method
A technology of software architecture and optimization methods, applied in software engineering design, genetic model, special data processing application, etc., can solve problems such as difficulty in obtaining performance improvement solutions, small performance improvement space, etc.
Active Publication Date: 2015-01-07
FUJIAN NORMAL UNIV
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However, in the optimization process, these methods do not fully consider the use times and order uncertainty of each rule in the rule combination scenario, so that they can only search for a relatively small performance improvement space, and it is often difficult to obtain the optimal performance improvement. Program
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[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0064] The rule-based software architecture layer performance evolution optimization method of the present invention uses a genetic algorithm to perform software architecture layer performance evolution optimization, such as figure 1 shown, including the following steps:
[0065] Step A. Perform population initialization
[0066] Set the population size, crossover probability, mutation probability, maximum evolutionary generation, number of iterations t =0, and adopt a certain individual encoding method to randomly generate the initial population P ( t ) in each individual; the individual coding schematic diagram is as follows figure 2 As shown, the individual encoding method is:
[0067] encoding of any individual Both use fixed-length natural number codes, and the code length l’ is defined by:
[0068]
[0069] inu i ...
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The invention relates to a rule-based software architecture layer performance evolution optimizing method. The rule-based software architecture layer performance evolution optimizing method uses a genetic algorithm to optimize the software architecture layer performance evolution and includes steps that A, performing population initialization; B, inputting individual codes (rue number sequences), an initial software architecture and a rule use history list, and calculating the fitness value of each individual in the population; C, using an elitism selection-based roulette wheel selection strategy to select; D, using one-point crossover with constraint checking mechanism to cross; E, using one-point variation with constraint checking mechanism to perform variation; F, judging whether meeting stopping conditions, if so, turning to a step G, otherwise, turning to a step E to obtain the next generation of population, and returning to the step B to perform the next iteration; G, removing 0 and rule numbers without improved effect from the optimal individual to obtain the optical improvement scheme and output. The rule-based software architecture layer performance evolution optimizing method is capable of lowering the software architecture layer performance optimizing cost and improving the optimizing quality.
Description
technical field [0001] The invention relates to the technical field of software performance optimization, in particular to a rule-based software architecture layer performance evolution optimization method. Background technique [0002] The performance of software is an important attribute to measure the quality of software system, and the quality of performance has become the key factor for the success or failure of the system. Performance optimization in the software architecture (Software Architecture, abbreviated as SA) design phase can detect performance problems such as high resource usage, long response time, and low throughput as early as possible, and alleviate or eliminate them through corresponding design improvements These problems, so as to obtain the SA design scheme that meets the performance requirements, and then achieve the purpose of performance optimization in the early stage of the software life cycle. Performance optimization based on SA can not only s...
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IPC IPC(8): G06F17/30G06N3/12
CPCG06F8/443
Inventor 杜欣倪友聪叶鹏谢大同肖如良汪春燕昂凤平王晓红李松
Owner FUJIAN NORMAL UNIV