基于改进边际优化的离散变量优化设计算法

Discrete variable optimization design algorithm based on improved marginal optimization

针对传统离散变量优化方法存在的目标函数测算次数多、收敛性不佳等问题,借鉴边际优化理论和模式搜索算法,设计了一种基于改进边际优化的离散变量优化设计算法。借鉴边际效用优化原理,通过引入周围单位步长空间的概念,在初始点选择、边际增量设计、禁忌搜索策略等方面进行了改进,并设计了变异操作以跳出局部最优。实例分析表明,所提算法能够快速准确地收敛到局部最优解,实现以尽可能少的目标函数测算得到问题的满意解或最优解,适合于求解高维离散变量优化问题和仿真优化问题。

Aiming at the problems of the traditional discrete variable optimization method such as too many times of objective function calculation and poor convergence,a discrete variable optimization design algorithm based on improved marginal optimization learning from marginal optimization theory and pattern search algorithm is designed.Based on the principle of marginal utility optimization,the concept of unit step space is introduced to improve the selection of initial point,marginal increment design,tabu search strategy,and mutation operation is designed to jump out of local optimum.Case studies show that the proposed algorithm can quickly and accurately converge to the local optimal solution,and the satisfactory solution or optimal solution can be obtained with as few objective functions as possible,which is suitable for solving high-dimensional discrete variable optimization problems and simulation optimization problems.