Priority encoding-based hybrid genetic algorithm for solving job-shop scheduling problem

Abstract

The invention provides a priority encoding-based hybrid genetic algorithm for solving a job-shop scheduling problem. According to the hybrid algorithm, an ant colony optimization algorithm (ACO) is combined with an adaptive genetic algorithm (AGA), the adaptive genetic algorithm is firstly executed by adopting a dynamic linking strategy, a group of optimization schemes generated according to the genetic algorithm are transformed into initial pheromone distribution of an ant colony algorithm and the ant colony algorithm is executed. A priority matrix-based encoding mode is adopted by the genetic algorithm, encoding is facilitated and decoding is not needed. Crossover and mutation probabilities adaptively change according to the change of the fitness of the best chromosome. The problems that the early convergence speed of the AGA is high and the late convergence speed is reduced due to shortage of feedback, and the early convergence speed of the ACO is relatively low due to shortage of pheromones and the randomness and the late convergence speed is significantly improved are solved. The shortcomings are overcome by learning from the strong points, the problems that search runs into a local optimal solution and the execution time is overlong are solved and the performance and the practicability of the algorithm are strengthened.

Description

[0001] Technical field [0002] The invention relates to the field of computer-executed manufacturing systems, in particular to using algorithms to solve the combined optimization problem of job shop scheduling. Background technique [0003] Job-Shop Scheduling Problem (Job-Shop Scheduling Problem) is one of the core and focus of manufacturing execution system research, its research not only has great practical significance, but also has far-reaching theoretical significance. The Job Shop Scheduling Problem (JSP) is to rationally allocate product manufacturing resources according to product manufacturing needs, and then achieve the purpose of rationally utilizing product manufacturing resources and improving enterprise economic benefits. JSP is a problem that coexists in the product manufacturing industry. It is closely related to the factory management and product manufacturing levels of Computer Integrated Manufacturing Systems (CIMS), and is an important research topic in t...

AI Technical Summary

Problems solved by technology

Due to the shortage and randomness of pheromone in the early stage of ACO algorithm, the convergence speed is low, but the convergence speed increases significantly in the later stage.

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