The application of improved adaptive genetic algorithm in the optimization of discrete variables

Abstract

In view of the problem of optimization design of complex discrete variables in practical application, the optimization design method of discrete variables and the processing methods of discrete variables are analyzed. The shortcomings of these methods are summarized, and the characteristics of genetic algorithms and discrete variables are combined to propose a self-adaptive discrete non-uniform crossover operator and adaptive discrete mapping mutation operator, so that the genetic operation can be implemented in discrete space for higher efficiency search. Combined with the optimal individual protection strategy and adaptive crossover and mutation strategy execution sequence, accelerate the population rate of global optimization, overall improve the efficiency of the algorithm of discrete optimization, through the design of a practical discrete variable optimization problems research, verified the effectiveness of the algorithm to solve the problem of discrete optimization design.