Improved TPX based IAGA for solving hybrid flow-shop scheduling problem with identical parallel machine

Abstract

The hormone regulation adaptive genetic algorithm based on improved two-point crossover (ITPX) is investigated and applied to a hybrid flow shop scheduling problem with identical parallel machines. Firstly, the hormone regulation mechanism is used to improve the parameter settings of different operators in the genetic algorithm to make it have adaptive regulation capability. Secondly, according to the problems of high redundancy and low efficiency of the traditional two-point crossover (TPX) operation, an exact point taking method is proposed to improve the exploration performance of the TPX operator, while multiple perturbation operations are designed to maintain the diversity characteristics of the variants. Finally, the improved algorithm is tested on the hybrid flow-shop scheduling problem with identical parallel machine. The test results show that the improved algorithm has an average percent deviation of 0.86% in solving the simple problem and 2.79 % in solving the complex problem, both of which are better than the comparable algorithms, verifying the effectiveness of the proposed algorithm.