Improved particle swarm optimization (PSO) algorithm of solving zero-waiting flow shop scheduling problem

A technology for improving particle swarm and workshop scheduling. It is applied in computing, computing models, instruments, etc. It can solve the problems of local optimization, fast convergence speed, and precociousness, so as to avoid premature convergence, improve the particle swarm optimization algorithm, and improve the overall situation. The effect of search ability

Active Publication Date: 2018-05-18
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 algorithm 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 it is easy to fall into premature and local optimum problems in the later stage

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  • Improved particle swarm optimization (PSO) algorithm of solving zero-waiting flow shop scheduling problem
  • Improved particle swarm optimization (PSO) algorithm of solving zero-waiting flow shop scheduling problem
  • Improved particle swarm optimization (PSO) algorithm of solving zero-waiting 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 (PSO) algorithm of solving the zero-waiting flow shop scheduling problem. Firstly, parameter initialization and population initialization are carried out, wherein initial workpiece sequences are generated, then a factorial encoding method is used to map all permutations to integers to form an initial population, and finally, a feasible initial velocity set is randomly generated; particles are moved; the population is updated through an original PSO population updating strategy, a new population is mapped to corresponding workpiecesequences, and work completion time of each new workpiece sequence is evaluated; an improved variable neighborhood search (VNS) algorithm is used for a local search, and results obtained by the search are used for replacement; a population adaption (PA) operator is used to increase diversity of the population; and checking of a termination condition is carried out, if the termination condition ismet, a process is stopped, and values of variables and corresponding sequences are returned to be used as a final solution, and otherwise, particle velocity is continuously updated. The method has the advantages of improving a particle swarm optimization algorithm, improving global search capability, and avoiding too early convergence.

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 Applications(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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