实数编码量子共生演算法及其在云任务调度中的应用

Real-coded quantum SOS algorithm and its application in cloud task scheduling

针对共生演算法收敛慢和易陷入局部最优的问题,结合量子遗传算法理论,提出一种实数编码的量子共生演算法(real-coded quantum symbiotic organisms search,RQSOS)。首先依据三角模糊数提出差异度概念,并依此构造一个以自变量向量的分量和一对概率幅为等位基因的三倍染色体,使一条染色体携带更多信息并增强解的多样性;然后提出一种基于阿基米德螺旋线的探索学习模式,加强对解空间的探索精度;最后使用共生演算法更新差异度值并依据差异度值对种群进行学习和变异操作,促使整个种群快速向最优方向进化且减小了陷入局部最优的概率。利用数值优化问题和云任务调度问题对算法进行验证,仿真结果表明,RQSOS算法在收敛速度和寻优能力上均有明显提升,是一种可行有效的算法。

In order to solve the problem that symbiotic organisms search algorithm converge slowly and easy to fall into the local optimum,combining quantum genetic algorithm theory,this paper proposed a real-coded quantum symbiotic organisms search algorithm(RQSOS).First,this paper presented the concept of the difference degree based on the principle of triangular fuzzy number,and constructed a variable component vector and a pair of probability amplitude of a allele in a chromosome that could carry more information and enhance the diversity of the solutions.Then it proposed the mode of rotary learning based on the Archimedes spiral,which strengthened the exploration ability of the solution space.Finally it updated the difference degree based on SOS,and carried out the population learning and mutation operations based on the value of the difference degree which could make the whole population evolution rapidly towards the optimal direction and reduced the probability of falling into local optimum.It was verified by numerical optimization and cloud task scheduling problem,and the simulation results show that the RQSOS algorithm can significantly improve the convergence speed and optimization ability,which is a feasible and effective algorithm.