Method for scheduling flow shop based on multi-swarm hybrid particle swarm algorithm

A hybrid particle swarm and workshop scheduling technology, applied in the computer field, can solve problems such as inability to overcome local search capabilities, fast evolution of PSO, and reduce population diversity, achieving strong local search capabilities, overcoming poor local search capabilities, and good scheduling The effect of the program

Inactive Publication Date: 2011-10-19
XIDIAN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because the number of individuals in the population is small, and the PSO evolution rate is fast, and only one local search operator is used at the same time, the diversity of the population is greatly reduced, and eventually the algorithm cannot be avoided from falling into a local op

Method used

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  • Method for scheduling flow shop based on multi-swarm hybrid particle swarm algorithm
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  • Method for scheduling flow shop based on multi-swarm hybrid particle swarm algorithm

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Experimental program
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Effect test

Embodiment 1

[0062] The present invention is a flow shop scheduling method based on multi-group mixed particle swarm algorithm, referring to figure 1 , including the following steps:

[0063] Step 1: Set parameters, the set parameters include: the maximum number of iterations of the program running t max , the number of scheduled jobs n, the number of machines m, the number of sub-populations S (when the number of jobs to be scheduled is large, S is relatively large), the maximum value of the particle position x max and minimum x min , the initial temperature T of the simulated annealing algorithm 0 , the annealing coefficient d, the use probability r1 of local search operator 1, the use probability r2 of local search operator 2, and define the fitness value f=T of particle A, where T is the corresponding scheduling scheme after decoding the position vector of particle A Scheduling time, initialize i=1;

[0064] Step 2: Generate the initial population Q, and divide the initial populati...

Embodiment 2

[0117] Simulation

[0118] The flow shop scheduling method based on the multi-population mixed particle swarm algorithm is the same as embodiment 1, and the effect of the present invention can be further illustrated by the following experiments:

[0119] 1. The standard test data set used in the simulation experiment

[0120] The present invention simulates and tests 26 flow shop scheduling problems, the first 8 problems are proposed by Carlier, and the other 18 problems are proposed by Reeves. These questions are commonly used test questions by scholars to study the flow shop scheduling problem. Among them, C in the table is the optimal time for each problem obtained so far.

[0121] 2. The parameter setting conditions of the simulation experiment:

[0122] The settings of the parameters involved in the present invention are as follows: the number of subpopulations S=3, the inertia weight w=1.0, the learning factor c1=c2=2.0, the minimum value xmin=-4.0 of the particle pos...

Embodiment 3

[0136] The flow shop scheduling method based on the multi-population mixed particle swarm algorithm is the same as that of Embodiment 1, and its parameter setting is the same as that of Embodiment 2. The proposed method (MPSOMA) is compared with the existing statistical learning-based particle swarm algorithm (PSOEDA) to verify the effectiveness of the introduced local search operator.

[0137] It can be seen from Table 2 that the present invention adopts multiple local search operators, and compared with the existing method PSOEDA, it has achieved better average values ​​on most of the problems. The invention can reasonably arrange the work sequence, use less completion time, and effectively improve the production efficiency of the flow workshop.

[0138] Table 2 The method (MPSOMA) of the present invention and the result contrast of PSOEDA

[0139]

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Abstract

The invention belongs to the computer field, and discloses a method for scheduling a flow shop based on a multi-swarm hybrid particle swarm algorithm, which solves the problems that the flow shop scheduling method based on the hybrid particle swarm algorithm is easy to result in premature convergence and local optimum. The method comprises the following steps of: setting parameters and generating S sub-swarms; judging whether the terminal condition is satisfied, if so, outputting a current optimum scheduling scheme, otherwise, updating positions of particles in each sub-swarm with the particle swarm algorithm, carrying out a local search on odd and even sub-swarms respectively by using searching operators 1 and 2 to obtain an optimum scheduling sequence of each sub-swarm; sharing information of the obtained optimum scheduling sequence by using a statistics-based probability model; and optimizing an optimum working sequence with a simulated annealing algorithm. In the invention, multiple swarms are added, the local search is carried out by using different searching operators, a good flow shop scheduling scheme is obtained, the production time is shortened, and the method can be used for the selection of the job shop scheduling scheme.

Description

technical field [0001] The invention belongs to the field of computers, and further relates to a method for processing a flow shop scheduling problem using an evolutionary algorithm in the field of artificial intelligence technology, specifically a flow shop scheduling method based on a multi-group mixed particle swarm algorithm. The method can be used in logistics, transportation, assembly line production and other fields to determine the priority of each operation in the production process and control the production process to reduce the time for the production system to complete all work sequences and improve production efficiency. Background technique [0002] Flow shop scheduling is a waiting-free shop scheduling problem. In the link of production practice, each work sequence contains several jobs, and each job contains several processes. Each job is completed on a different machine. On the same machine, different jobs are completed in the same order. After one process...

Claims

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Application Information

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IPC IPC(8): G06N3/12
Inventor 刘若辰唐丽娜焦李成李阳阳公茂果马文萍王爽朱虎明
Owner XIDIAN UNIV
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