集成化服务链多目标全局优化模型与算法

Research on multi-objective global optimization model and algorithm of integrated service chain

基于集成化服务链网络模型和候选服务资源评价指标,建立集成化服务链的多目标全局优化模型,并提出一种基于改进多目标遗传算法的集成化服务链多目标全局优化算法。算法采用基于距离的无参数种群多样性度量算子,在适应值分配、精英保持和选择操作中均进行了种群多样性控制,能在满足多约束条件下同时优化多个目标,得到一组满足决策者不同主观偏好的Pareto全局最优解集。仿真实验表明算法具有全局收敛性并具有较好的解的质量和分布,能有效求解集成化服务链多目标全局优化问题。

Based on the network model of integrated service chains and evaluation index of candidate service resources, optimizing integrated service chain can be formally defined as a multi-objective global optimization model with multiple constraints. We propose a multi-objective global optimization algorithm based on improved multi-objective genetic algorithms. The proposed algorithm uses a distance-based nonparametric population diversity measurement operator, and diversity control is involved in the process of adaptive value assignment, elitist maintaining and selection operation. The proposed algorithm can optimize multiple objectives at the same time on the premise of meeting the constraints, and finally get a constrained Pareto optimum solution set which satisfy decision makers＇ prefers. The simulation experiments indicate that the proposed algorithms can achieve global convergence and has better solution quality and distribution, which efficiently solve the problem of integrated service chain multi-objective global optimization.