求解物流Web服务组合问题的两阶段多目标蚁群算法

发布时间：2019-03-22 11:13

【摘要】：针对基于QoS的物流Web服务组合优化问题,提出了两阶段多目标蚁群优化(TMACO)算法。首先,针对原始数据集中存在被支配候选服务而增加算法求解时间的问题,提出了基于Pareto支配的预优化策略;其次,针对属性权重难以确定的问题,提出了不依赖权重的信息素更新策略和启发信息策略;最后,针对基础蚁群算法容易陷入局部最优的问题,提出了懒蚂蚁策略。实验结果表明,TMACO算法具有良好性能,相对于基础蚁群算法、利用解与理想解距离来更新信息素的改进蚁群算法、遗传算法以及用支配程度作为解的个体评价的改进遗传算法,TMACO算法有更高的寻优能力,能够找到更多更优的非劣解。
[Abstract]:A two-stage multi-objective ant colony optimization (TMACO) algorithm was proposed for the optimization of logistics Web service composition based on QoS. Firstly, a pre-optimization strategy based on Pareto is proposed to solve the problem that there is a dominant candidate service in the original data set and the solution time of the algorithm is increased. Secondly, aiming at the problem that the attribute weight is difficult to be determined, the pheromone updating strategy and heuristic information strategy are put forward, which are independent of the weight. Finally, aiming at the problem that the basic ant colony algorithm is prone to fall into local optimization, a lazy ant strategy is proposed. The experimental results show that the TMACO algorithm has good performance. Compared with the basic ant colony algorithm, the improved ant colony algorithm using the distance between the solution and the ideal solution to update pheromone, the genetic algorithm and the improved genetic algorithm for individual evaluation with dominance degree as the solution are used to update the pheromone. TMACO algorithm has higher optimization ability and can find more and better non-inferior solutions.
【作者单位】：合肥工业大学;
【基金】：国家自然科学基金资助项目(71301041,71271071)
【分类号】：TP18;TP393.09

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