基于MapReduce离散萤火虫群优化算法的服务选择方法

摘要

针对云计算环境下求解大规模的Web服务选择问题,提出基于MapReduce模式的并行离散萤火虫群优化算法.该算法重新定义个体的编码,计算个体间的距离,改进位置更新,提高高维空间中的搜索能力,并采用分群分治思想和理想点方法进行优化,以避免过早陷入局部最优,提高处理大规模问题的能力.实验结果表明,该算法在求解服务选择问题上具有可行性和有效性,且扩展性较好.%This paper presents a parallelizing discrete Glowworm Swarm Optimization algorithm based on MapReduce (MRDGSO) to solve the large-scale Web service selection problem in cloud computing environment.The algorithm redefines individual coding,calculates the distance between individuals,and improves their position update,which increases the search capability in the high-dimension space.The optimization is based on the idea of divide conquer and group and ideal point to avoid falling into local optimum,and improve the ability to deal with large scale problems.Experimental results indicate the feasibility and efficiency of the presented algorithm to solve Web service selection problem and demonstrate the superiority of the algorithm in the scalability.