一种基于Levy飞行的改进蝗虫优化算法

An Improved Grasshopper Optimization Algorithm Based on Levy Flight

蝗虫优化算法是一种元启发式优化算法,能够用于解决任务调度问题。已有的改进蝗虫优化算法缺乏随机性,跳出局部最优的能力较弱,改进效果不够显著。针对这一问题,本文提出一种基于Levy飞行的改进蝗虫优化算法(LBGOA)。该算法引入基于Levy飞行的局部搜索机制增强算法的随机性,并采用基于线性递减参数的随机跳出策略来提高算法跳出局部最优的能力。CEC测试实验结果表明,所提出的算法拥有较强的搜索能力,在30个测试函数结果中能够获得17个最优解和6个次优解。将所提出的改进算法应用于边缘计算中的任务调度问题。任务调度仿真实验结果表明,所提出的算法能够有效提高搜索效果,相比GOA、OBLGOA、WOA、ALO、DA和PSO算法,LBGOA的搜索效果分别提升7.4%、7.5%、4.8%、27.7%、29.9%和20.7%。

Grasshopper optimization algorithm is a meta-heuristic optimization algorithm that can be used to solve task scheduling problems. The existing improved grasshopper optimization algorithm lacks randomness,and its ability to jump out of local optimum is weak. The improvement effect is not obvious enough. To solve this problem,this paper proposes an improved Grasshopper Optimization Algorithm Based on Levy flight( LBGOA). The algorithm introduces a local search mechanism based on Levy flight to enhance the randomness of the algorithm and adopts a random jumping strategy based on linear decreasing parameters to improve the ability of the algorithm to jump out of local optimum. The experimental results of the CEC test show that the proposed algorithm has strong search ability,and 17 optimal solutions and 6 suboptimal solutions are obtained by LBGOA in the results of30 test functions. The proposed improved algorithm is applied to the task scheduling problem in edge computing. The results of task scheduling simulation experiments show that the proposed algorithm can effectively improve the search results. Compared with GOA,OBLGOA,WOA,ALO,DA and PSO algorithms,the search results by LBGOA are promoted by 7. 4%,7. 5%,4. 8%,27. 7%,29. 9%,and 20. 7% respectively.