基于超级账本的蚁群因子差分进化算法的可信服务组合优化

为了保障服务组合优化过程中的QoS数据的真实性，提出了一种基于超级账本平台的可信框架；同时为了提高服务组合的优化效率，提出了一种蚁群因子的差分进化算法的服务组合优化方法(ACOF-DE)。首先，在超级账本平台上部署相应节点，构建可信框架，保障候选服务的真实性；然后，将所提出的算法以智能合约的形式，在区块链上对服务组合的优化问题进行求解，使组合过程在可信的环境下执行。该算法通过引入多种蚁群因子，比如蚁群路径因子、最优蚁群因子、信息素因子以及基于蚁群因子的差分计算，帮助算法动态控制搜索空间、记录迭代过程中的关键信息，以提高算法优化能力。最后，通过仿真实验证明可信框架可以有效地保障数据的可信；ACOF-DE相比其他智能优化算法拥有更佳的优化效率。

Ant colony factor differential evolutionary algorithm based on Hyperledger Fabric for trustworthy service composition optimization

In order to guarantee the authenticity of QoS data in the process of service composition optimization, this paper proposed a trusted framework based on the Hyperledger Fabric; meanwhile, this paper proposed a service composition optimization method of differential evolution algorithm with ant colony factor to improve the efficiency of service composition optimization. Firstly, this paper deployed corresponding nodes on the Hyperledger Fabric to build a trusted framework that guaranteed the authenticity of the candidate services; then, in the form of a smart contract on the blockchain, the proposed algorithm solved the optimization problem for the combination of services, enabling the composition process could be execute in a trusted environment. The algorithm helped the algorithm dynamically control the search space and record the key information in the iteration process by introducing various ACO factors, such as the ACO path factor, the optimal ACO factor, the pheromone factor and the difference calculation based on the ACO factors, in order to improve the optimization capability of the algorithm. Finally, the simulation experiments demonstrate that the trustworthy framework can effectively guarantee the trustworthiness of data; ACOF-DE has better optimization efficiency compared with other intelligent optimization algorithms.