Distributed flexible flow shop scheduling system and method, terminal and medium

Abstract

The invention belongs to the technical field of data processing, and discloses a distributed flexible flow shop scheduling system and method, a terminal and a medium. The distributed flexible flow shop scheduling system based on the knowledge-driven learning type king butterfly optimization method comprises a knowledge-driven learning type king butterfly optimization module, a distributed assembly flexible flow shop scheduling sequence generation module, an initialization module, an applicability calculation module and an output module. The empire butterfly optimization algorithm of a knowledge-driven learning mechanism can be effectively evolved, and has a self-learning capability. Neighborhood information extracted from the candidate solution is regarded as priori knowledge of the KDLMBO algorithm. The learning mechanism is composed of a learning migration operator and a learning butterfly adjustment operator. And then, in the iteration process of the algorithm, realizing collective intelligence of self-learning by the two cooperative operators. Experimental results prove the efficiency and significance of the KDLMBO algorithm provided by the invention.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a distributed flexible flow shop scheduling system, method, terminal and medium. Background technique [0002] Currently, various real-world applications are transformed into continuous optimization problems, which become more complex as the problem scales, and are difficult to solve by traditional optimization algorithms. Current research focuses on feasible and efficient optimization algorithms for solving continuous optimization problems. As the problem scale increases, a continuous optimization problem is defined as min f(x), x = [x1,x2,…,xD] , where the goal is to find the maximum or minimum value f(x) and output the optimal solution x. In order to directly solve the continuous optimization problem, researchers have proposed various optimization algorithms. However, when dealing with these continuous optimization problems, the performance of traditional ...

AI Technical Summary

Problems solved by technology

The mathematical programming method is a search method that implicitly enumerates the entire space, and the search efficiency is low

Although this method can provide the optimal solution to the problem in theory, it is difficult to solve large-scale scheduling problems in a limited time

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