Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem

Ding, Haojie; Gu, Xingsheng

doi:10.1016/j.cor.2020.104951

Cited by 79 publications

(30 citation statements)

References 28 publications

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“…Currently, there are many research achievements in this direction, and more advanced methods include genetic algorithm [3], tabu search [4], neural network [5], heuristic algorithm [6], particle swarm optimization [7], bionics algorithm [8], and various hybrid algorithms [9]. With the improvement of people's living standards, today's consumers increasingly pursue personalized products.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence

Zhang

Wang

et al. 2020

Complexity

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This paper is in view of the current algorithm to solve Multiple-Devices-Process integrated scheduling problems without considering parallel process between the parallel processing. Meanwhile, the influence of the first processing procedure on the second processing procedure is ignored, resulting in poor tightness between serial processes and poor parallelism between parallel processes, ultimately affecting the product scheduling results. We proposed Multiple-Devices-Process integrated scheduling algorithm with time-selective for process sequence. The proposed Multiple-Devices-Process sequencing strategy determines the process of scheduling order and improves the tightness between serial process. This paper presents a method to determine the quasischeduling time point of the multiequipment process, the time-selective strategy of Multiple-Devices-Process, and the time-selective adjustment strategy of Multiple-Devices-Process so that the first and the second processing processes cooperate with each other, and the purpose of improving the tightness of the serial process and the parallelism of the parallel processes is achieved, so as to shorten the product processing time.

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Section: Introductionmentioning

confidence: 99%

Multiple-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence

Zhang

Wang

et al. 2020

Complexity

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“…The credibility analysis of Web big data can be carried out based on intelligent algorithm. The analysis results have visualized and displayed externally [6].…”

Section: Framework Of Web Big Datamentioning

confidence: 99%

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Zhao

2022

JWE

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In order to improve the credibility evaluation effectiveness of web big data information, the improved particle swarm optimization is established. Firstly, framework of web big data is designed to include web big data source, data storage, data processing and data analysis. The global credibility calculation formula of whole web is established. Secondly, the improved particle swarm algorithm is constructed through updating weight and training factor, introducing cross and mutation operations into the algorithm, and improving population diversification based on mountain climbing algorithm. Finally, ten sample industries and one hundred sample stocks are selected to carry out experiment analysis, and the results show that the proposed algorithm can effectively distinguish trusted and untrustworthy records. And the proposed algorithm of calculating credibility of web big data has high reasonableness.

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“…Firstly, the locatable area of pseudolite system is determined. DEM data of longitude, latitude, and elevation terrain of the target area are imported and converted into a twodimensional plane coordinate system relationship, as shown in Equations ( 3)-( 5) [21].…”

Section: Objective Function Designmentioning

confidence: 99%

Research on the Station Layout Method of Ground‐Based Pseudolite Positioning System Based on NSGA‐II Algorithm

Yang

Sun

2021

Wireless Communications and Mobile Computing

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Given the problem that the existing method of station distributing the pseudosatellite system cannot ensure both its coverage and position in a situation of signal occlusion, it proposed a new stationary layout method with an elite strategy for a ground-based pseudosatellite positioning system based on the elite strategy of the nondominant genetic rankings (NSGA-II). The geometrical design of the pseudosatellite system is calculated by visual domain analysis and precision factors for the signal coverage age and base station. To optimize the algorithm, the NSGA-II algorithm is used. An earth pseudosatellite positioning system method of stationary distribution is obtained that simultaneously optimizes signal coverage and positioning accuracy. The algorithm is better distributed and has a certain superintendence compared with the traditional genetic algorithm.

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Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem

Cited by 79 publications

References 28 publications

Multiple-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence

Multiple-Devices-Process Integrated Scheduling Algorithm with Time-Selective Strategy for Process Sequence

Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Research on the Station Layout Method of Ground‐Based Pseudolite Positioning System Based on NSGA‐II Algorithm

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