基于双种群协同进化的QoS全局最优Web服务选择算法
发布时间：2018-08-16 12:30
【摘要】：针对服务质量(quality of service,QoS)全局最优Web服务选择问题,提出了一种双种群协同进化QoS全局最优Web服务选择算法。算法在多目标离散粒子群算法基础上设计一种双种群协同进化框架以同步进行非支配排序和精英粒子保留,并定义了一种新的离散粒子位置更新算子。同时为保证粒子的多样性和算法的全局收敛能力,算法采用基于距离的粒子多样性度量算子、基于适应值排序的粒子选择算法和基于轮盘赌的全局最优解选择策略。仿真实验结果表明该算法能同时优化多个目标,并得到一组满足约束的Pareto最优解,且具有较好的性能和鲁棒性,解集的质量和分布也优于非支配排序遗传(nondominated sorting genetic algorithm,NSGA)算法的改进算法NSGA-Ⅱ,能有效解决QoS全局最优的Web服务选择问题。
[Abstract]:Aiming at the globally optimal (quality of service selection problem, a two-population coevolutionary QoS global optimal Web service selection algorithm is proposed. Based on the multi-objective discrete particle swarm optimization algorithm, a two-population coevolutionary framework is designed to synchronize non-dominated ordering and elite particle retention, and a new discrete particle location update operator is defined. In order to ensure the diversity of particles and the ability of global convergence, the algorithm adopts a distance based particle diversity metric operator, a particle selection algorithm based on fitness ranking and a global optimal solution selection strategy based on roulette. Simulation results show that the algorithm can optimize multiple targets at the same time, and obtain a set of Pareto optimal solutions which satisfy the constraints, and have good performance and robustness. The quality and distribution of the solution set is also superior to the improved algorithm NSGA- 鈪，
本文编号：2185990
[Abstract]:Aiming at the globally optimal (quality of service selection problem, a two-population coevolutionary QoS global optimal Web service selection algorithm is proposed. Based on the multi-objective discrete particle swarm optimization algorithm, a two-population coevolutionary framework is designed to synchronize non-dominated ordering and elite particle retention, and a new discrete particle location update operator is defined. In order to ensure the diversity of particles and the ability of global convergence, the algorithm adopts a distance based particle diversity metric operator, a particle selection algorithm based on fitness ranking and a global optimal solution selection strategy based on roulette. Simulation results show that the algorithm can optimize multiple targets at the same time, and obtain a set of Pareto optimal solutions which satisfy the constraints, and have good performance and robustness. The quality and distribution of the solution set is also superior to the improved algorithm NSGA- 鈪，
本文编号：2185990
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