Multi-strategy particle swarm optimization method and system for solving permutation flowshop scheduling problem

Abstract

The invention belongs to the optimized control field and discloses a multi-strategy particle swarm optimization method and system for solving a permutation flowshop scheduling problem. The multi-strategy particle swarm optimization method comprises steps of constructing a diversified evaluation strategy, measuring population diversity of particle colonies according to an information entropy mode through subintervals divided by gravitational values, directing alternative execution of local exploration and global searching during an optimizing process, during global optimal particle selection which borrows an ant-city selection strategy in a particle speed-displacement model, calculating a probability of each particle choosing another particle as a global optimal particle, determining a global optimal particle which is following during a particle moving process according to the probability, designing a set variation, and guiding the particle swarm to jump out of a local optimal solution area to perform particle swarm global searching. The set variation operation of the multi-strategy particle swarm optimization method can better help the particle to escape which falls into the local optimal region, prevents a premature convergent phenomenon, can perform continuous optimizing in a global range and can obtain a better result.

Description

technical field [0001] The invention belongs to the fields of optimization control, artificial intelligence and manufacturing, and in particular relates to a multi-strategy particle swarm optimization method and system for solving replacement flow workshop scheduling. Background technique [0002] Job-Shop Scheduling (JSS) is the basis and key to realize advanced manufacturing and improve production efficiency. It is one of the core technologies and hot issues in the research of enterprise manufacturing systems. Under the constraints, the processing order and processing time of each workpiece on different equipment are determined to ensure the optimization of the selected production target. However, due to the complex modeling, complex calculation and multi-constraint characteristics of job shop scheduling, this problem is a typical combinatorial optimization problem with NP complexity. [0003] The replacement flow shop scheduling problem is a simplified model of the job s...

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Problems solved by technology

[0006] To sum up, the problems in the existing technology are: how to judge the premature method of particle swarms, and how to maintain a good level of diversity maintenance in the optimization process, so that the exploration process of particle swarms in the search space can cover the whole The optimal solution, the existing technology lacks guidance; it is worth noting that any mutation method has inherent limitations, that is, the probability of "good" or "decay" of the mutant individual is within a certain interval; therefore, through a certain This mutation method guides the particles to jump out of the local optimal region, that is, there is the possibility of success and the probability of failure; the ability to truly fully realize the "escape" of the particle's local optimal solution region is also lack of theoretical proof in the prior art And practical application test basis; the existing particle optimization method is only applied to the job shop scheduling problem for the replacement flow shop scheduling problem, and cannot be applied to other optimization problems in many industrial situations at the same time, such as: resource allocation, pattern clustering, and robotics Practical optimization problems such as path planning; and for these optimization problems, the scheduling problem is not transformed into a combinatorial optimization problem with several performance constraints to be suitable for solving

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