Hybrid particle swarm tabu search algorithm for solving job-shop scheduling problem

Abstract

The invention provides a hybrid particle swarm tabu search algorithm for solving a job-shop scheduling problem. Compared with other meta-heuristic algorithms, the algorithm has the characteristic of ''elite memory'' according to a PSO and has the characteristic of fast convergence, the PSO is taken as an initial solution source of TSAB tabu search, and an encoding and decoding mechanism for mapping a particle swarm continuous solution space into a discrete space of the job-shop scheduling problem is designed. A real number solution of the PSO is converted into an integer solution of the tabu search algorithm through a real integer encoding method and the integer solution of the tabu search algorithm is converted into the real number solution of the PSO through a real integer decoding method after one-time iteration; and a chance of accurate search is made in a potential space to own more exploration in a global search space. An improved PSO with a balancing strategy is provided, and a balance operator beta is introduced. The performance of the algorithm is greatly strengthened through these improvements and the actual job-shop scheduling condition is combined. The algorithm is high in practicability and good in usability.

Description

[0001] Technical field [0002] The invention relates to the technical field of job shop scheduling, in particular to solving the job shop scheduling problem with an algorithm. Background technique [0003] The scheduling problem usually refers to how to use the existing resources to arrange production reasonably within the specified time, so as to maximize the production benefits. The workshop scheduling problem is a subset of the scheduling problem, and it is an important part of the production planning and control of the enterprise, and a key factor to help the enterprise improve its competitiveness. As science and technology continue to evolve, metaheuristic methods are proposed, the success of which depends on their ability to provide a balance between exploration (diversification) and exploitation (enhancement). According to their search strategies, metaheuristic methods can be divided into two categories: one is local search algorithms based on a single solution, inclu...

AI Technical Summary

Problems solved by technology

However, there are still some shortcomings in PSO: when the algorithm runs for a period of time, its running speed will slow down, and the efficiency of the solution is very low, which means that the algorithm has fallen into a local optimum, and it is difficult to find the optimum solved

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