Open AccessOptimization of operation sequencing based on GA-Jaya algorithm
Author(s) -
Kai Zheng,
Rui Zhang,
Ziqi Zhu,
Hua-Dong Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2113/1/012075
Subject(s) - crossover , ant colony optimization algorithms , meta optimization , particle swarm optimization , genetic algorithm , algorithm , mathematical optimization , computer science , population , population based incremental learning , operator (biology) , mutation , mathematics , artificial intelligence , biochemistry , chemistry , demography , repressor , sociology , transcription factor , gene
To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.