基于改进北极熊算法的制造云服务组合优化

Manufacturing cloud service composition optimization based on modified polar bear algorithm

为了提高云制造环境下制造服务组合优化的效率,提出了一种基于改进北极熊算法的制造云服务组合优化方法。该方法对制造服务进行实数编码,并以服务功能和服务质量为评价指标,使用改进的北极熊算法对制造云服务组合优化问题进行求解,得到最优的服务组合方案。同时通过引入动态视野,对算法的局部搜索进行调整,并与遗传算法中的变异策略相结合,以提高求解多目标问题的效率,同时降低因初始参数影响而导致算法陷入局部最优的可能。算例分析表明,改进的北极熊算法在求解制造云服务组合优化问题上比原始北极熊算法、标准遗传算法、改进的灰狼优化算法和改进的粒子群优化算法具有更高的效率。

In order to improve the efficiency of manufacturing service composition optimization in the cloud manufacturing environment,this paper proposed a composition optimization method based on the modified polar bear algorithm.This method encoded the manufacturing services in real numbers,took the service function and service quality as evaluation indicators,and used the modified polar bear algorithm to solve the optimization problem of manufacturing cloud service portfolio,and obtained the optimal service portfolio plan.At the same time,this article used the dynamic vision to adjust the local search of the algorithm,and combined it with the mutation strategy in the genetic algorithm.Through these improvements improved the efficiency of the algorithm for solving multi-objective problems and reduced the possibility of the algorithm falling into local optimum due to the influence of initial parameters.Example analysis shows that compared with the original polar bear algorithm,the standard genetic algorithm,the improved gray wolf optimizer and the improved particle swarm optimization algorithm,the modified polar bear algorithm is more efficient in solving the problem of manufacturing cloud service portfolio optimization.