Genetic Algorithm for QoS-Aware Web Service Selection Based on Chaotic Sequences

Chengwen Zhang, Yue Ma
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引用次数: 6

Abstract

As a kind of service selection algorithm, Genetic Algorithm is a good way to select an optimal composite plan from many composite plans. Including crossover operation, mutation operation and selection operation, all the executions of GA rely on a randomly search procedure to seek the area of possible solutions. But, bad convergence and prematurity phenomenon of GA are produced by random sequences generation. They have become the obstacle for GA’s further application. To improve the convergence of genetic algorithm (GA) for web service selection with global Quality-of-Service (QoS) constraints, chaos theory is introduced into the genetic algorithm with the relation matrix coding scheme. These chaotic laws are all based on the relation matrix coding scheme. During crossover and mutation process phases, chaotic time series are adopted instead of random ones. The effect of chaotic sequences and random ones is compared during several numerical tests. And, the performance of GA using chaotic time series and random ones is investigated. The simulation results on web service selection with global QoS constraints have shown that the proposed strategy based on chaotic sequences can enhance GA’s convergence capability. The fitness is also improved after the chaotic approaches are introduced.
基于混沌序列的qos感知Web服务选择遗传算法
遗传算法作为一种服务选择算法,是一种从众多组合方案中选择最优组合方案的好方法。遗传算法包括交叉操作、变异操作和选择操作,所有的遗传算法的执行都依赖于一个随机搜索过程来寻找可能解的区域。但随机序列生成会产生遗传算法的收敛性差和早熟现象。它们已经成为遗传算法进一步应用的障碍。为了提高遗传算法在全局服务质量(QoS)约束下web服务选择的收敛性,将混沌理论引入到遗传算法中,采用关系矩阵编码方案。这些混沌律都是基于关系矩阵编码方案。在交叉和突变过程阶段,采用混沌时间序列代替随机时间序列。在若干数值试验中比较了混沌序列和随机序列的影响。研究了混沌时间序列和随机时间序列的遗传算法性能。对具有全局QoS约束的web服务选择的仿真结果表明,基于混沌序列的策略可以提高遗传算法的收敛能力。引入混沌方法后,适应度也得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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