An improved particle swarm optimization method for solving the zero-wait flow shop scheduling problem

A technology for improving particle swarm and workshop scheduling, applied in data processing applications, instruments, artificial life, etc., can solve problems such as fast convergence speed, local optimum, premature maturity, etc., to avoid premature convergence, improve particle swarm optimization algorithm, Improve the effect of global search ability

Active Publication Date: 2021-07-30
LANZHOU UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0005] The purpose of the present invention is to provide an improved particle swarm optimization method for solving the zero-wait flow shop scheduling problem, which solves the problem that the traditional particle swarm algorithm has a fast initial convergence speed, but is prone to fall into premature and local optimum problems in the later stage

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  • An improved particle swarm optimization method for solving the zero-wait flow shop scheduling problem
  • An improved particle swarm optimization method for solving the zero-wait flow shop scheduling problem
  • An improved particle swarm optimization method for solving the zero-wait flow shop scheduling problem

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Embodiment Construction

[0040] The present invention will be described in detail below in combination with specific embodiments.

[0041] The inventive method step is as follows:

[0042] Step 1: Parameter initialization. Set the values ​​of the control parameters: L (number of particles), MRT (maximum run time limit), c 1 and c2 (velocity constant), w min and w max (parameters affecting inertial weight), c=0 (the number of times the population distance has not changed), g=1 (the current number of iterations).

[0043] Step 2: Population initialization. Use NN+NEH to generate the initial artifact arrangement. Evaluate its fitness value to get the current optimal solution pbest, the historical optimal solution gbest=pbest, and the Euclidean distance between populations D 0 . Then use the factorial encoding method to map L permutations to L integers, where L is the initial population size (consistent below). These L integers constitute the initial population. Finally, a set of feasible initial...

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Abstract

The invention discloses an improved particle swarm optimization method for solving the scheduling problem of a zero-wait flow shop. Firstly, parameter initialization and population initialization are performed to generate an initial workpiece sequence, and then the factorial coding method is used to map all permutations to integers to form an initial population. Finally, Randomly generate a feasible set of initial velocities; move the particles; update the population through the original PSO population update strategy, map the new population to the corresponding workpiece sequence, and evaluate the completion time of each new workpiece sequence. Use the improved variable neighborhood search algorithm to search locally, and replace the search results; use the population adaptive operator PA to increase the diversity of the population; check the termination condition, if the termination condition is met, stop, return the value of the variable and the corresponding The sequence of is taken as the final solution, otherwise continue to update the particle velocity. The beneficial effect of the invention is that the particle swarm optimization algorithm is improved, the global search ability is improved, and premature convergence is avoided.

Description

technical field [0001] The invention belongs to the technical field of flow shop scheduling algorithms, and in particular relates to using an algorithm to solve the zero-wait flow shop scheduling problem. Background technique [0002] 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 bas...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06N3/00
CPCG06N3/006G06Q10/0631
Inventor 赵付青杨国强宋厚彬何继爱唐建新姚毓凯张建林
Owner LANZHOU UNIVERSITY OF TECHNOLOGY
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