基于离散灰狼算法的资源分配问题求解

A solution to resource allocation problem based on discrete grey wolf optimizer

针对标准灰狼优化算法(GWO)只适合求解连续优化问题,无法直接求解离散域上的资源分配问题,提出一种基于马太效应的离散灰狼优化算法(DGWO)来求解资源分配问题.首先,根据数学映射思想给出一种将连续空间转化为离散空间、实数变换为整数的编码转换方法;然后,对其中的不可行解采用基于马太效应的修复与优化方法处理;最后,将DGWO计算结果与遗传算法结果进行对比发现不论是收敛速度,还是求解质量,DGWO算法均优于遗传算法.实验结果表明了DGWO算法求解资源分配问题的可行性、正确性和优越性.

As the original grey wolf optimizer(GWO) can only solve continuous optimization problems,but cannot directly solve the resource allocation problem in the discrete domain, a discrete grey wolf optimizer(DGWO) based on the Matthew effect was proposed to solve the resource allocation problem. Firstly, according to the mathematical mapping idea, a coding conversion method was given,which converted continuous search space into discrete search space and real numbers into integers.Then,the infeasible solution was treated by repairing and optimizing method based on Matthew effect.Finally,the results of DGWO were compared with those of genetic algorithm,and it can be seen that no matter the convergence speed or the solution quality,DGWO is superior to the genetic algorithm. The experimental results show the feasibility, correctness and superiority of DGWO for the resource allocation problem solving.