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An Efficient Discrete Invasive Weed Optimization Algorithm for Web Services Selection

机译：Web服务选择的高效离散杂草优化算法

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摘要

Efficient algorithm is required to select component services with end-to-end QoS constraints in dynamic composition of Web Services while optimizing the QoS of the composite service. In this paper, the services selection probelm is modeled as a nonlinear optimization with constraints, then a novel discrete invasive weed optimization web services selection algorithm is proposed. The proposed solution consists of two steps：first, a set of randomly generated feasible solutions are transformed into decimal code. Second, We utilize Gaussian diffusion to guide the population to spread in the solution space. The mutation probability and mutation step size of individual is dynamically adjusted by changing the stantard deviation of Gaussian distribution. Accordingly, the population diversity is ensured in the early stage to expand the search space, while the local search nearby excellent individuals is focused in the latter stage to ensure the global convergence. Theoretical analysis and experiment results indicate the efficiency, robustness and feasibility of our approach.
机译：在优化组合服务的QoS的同时，需要一种有效的算法来选择Web服务的动态组合中具有端到端QoS约束的组件服务。本文将服务选择模型建模为具有约束条件的非线性优化算法，然后提出了一种新的离散入侵杂草优化网络服务选择算法。所提出的解决方案包括两个步骤：首先，将一组随机生成的可行解决方案转换为十进制代码。其次，我们利用高斯扩散来指导总体在解空间中扩散。可通过更改高斯分布的标准偏差来动态调整个体的变异概率和变异步长。因此，在早期确保了种群多样性以扩大搜索空间，而在后期则集中在附近搜索附近的优秀个人以确保全球融合。理论分析和实验结果表明了该方法的有效性，鲁棒性和可行性。

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