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Scheduling optimization of flexible flow shop with buffer capacity limitation based on an improved discrete particle swarm optimization algorithm

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ENGINEERING OPTIMIZATION 2025年第2期57卷 571-597页

作者： Chang, Daliang Shi, Haibo Liu, Chang Meng, Fu Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

To address the challenge of production scheduling in flexible flow shops with buffer capacity limitations, a mathematical model is established to minimize the maximum completion time and maximize equipment utilization. To efficiently solve the problem, an improved discrete particle swarm optimization algorithm with multi-population reduction (MPREDPSO) is developed. It integrates an elite retention strategy, which significantly contributes to its efficiency and robustness. The algorithm stands out for its ability to effectively navigate through potential local optima, a common pitfall in optimization problems, through a Euclid algorithm coupled with a sophisticated gene backtracking mechanism. The performance of MPREDPSO is rigorously tested and proven through comparative analyses. Its effectiveness is proven through comparisons with other algorithms, namely the discrete particle swarm algorithm, genetic algorithm, whale algorithm, grey wolf optimizer and whale swarm algorithm. In addition, MPREDPSO's practical utility is demonstrated in a high-temperature ham sausage production workshop, showcasing its industrial application capabilities.

关键词： Flexible flow shop multi-population reduction discrete particle swarm algorithm elite retention

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Tidal Turbine Array Optimization Based on the discrete particle swarm algorithm

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China Ocean Engineering 2018年第3期32卷 358-364页

作者： WU Guo-wei WU He WANG Xiao-yong ZHOU Qing-wei LIU Xiao-man National Ocean Technology Center Tianjin 300112China Satellite Environment Center Ministry of Environmental ProtectionBeijing 100094China

In consideration of the resource wasted by unreasonable layout scheme of tidal current turbines, which would influence the ratio of cost and power output, particle swarm optimization algorithm is introduced and improved in the paper. In order to solve the problem of optimal array of tidal turbines, the discrete particle swarm optimization(DPSO) algorithm has been performed by re-defining the updating strategies of particles’ velocity and position. This paper analyzes the optimization problem of micrositing of tidal current turbines by adjusting each turbine’s position,where the maximum value of total electric power is obtained at the maximum speed in the flood tide and ebb ***, the best installed turbine number is generated by maximizing the output energy in the given tidal farm by the Farm/Flux and empirical method. Secondly, considering the wake effect, the reasonable distance between turbines,and the tidal velocities influencing factors in the tidal farm, Jensen wake model and elliptic distribution model are selected for the turbines’ total generating capacity calculation at the maximum speed in the flood tide and ebb ***, the total generating capacity, regarded as objective function, is calculated in the final simulation, thus the DPSO could guide the individuals to the feasible area and optimal position. The results have been concluded that the optimization algorithm, which increased 6.19% more recourse output than experience method, can be thought as a good tool for engineering design of tidal energy demonstration.

关键词： tidal power wake model turbine layout discrete particle swarm algorithm

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A discrete particle swarm algorithm for OLAP data cube selection

A discrete particle swarm algorithm for OLAP data cube selec...

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8th International Conference on Enterprise Information Systems (ICEIS 2006)

作者： Loureiro, Jorge Belo, Orlando Escola Super Tecnol Viseu Dept Informat Inst Super Politecn Viseu Campus Politecn Repeses Viseu Portugal Univ Minho Dept Informat Escola Engn P-4710 Braga Portugal

ISBN: (纸本)9728865414

Multidimensional analysis supported by Online Analytical Processing (OLAP) systems demands for many aggregation functions over enormous data volumes. In order to achieve query answering times compatible with the OLAP systems' users, and allowing all the business analytical views required, OLAP data is organized as a multidimensional model, known as data cube. The materialization of all the data cubes required for decision makers would allow fast and consistent answering times to OLAP queries. However, this also imply intolerable costs, concerning to storage space and time, even when a data warehouse had a medium size and dimensionality-this will be critical on refreshing operations. On the other hand, given a query profile, only a part of all subcubes are really interesting. Thus, cube selection must be made aiming to minimize query (and maintenance) costs, keeping as a constraint the materializing space. That is a complex problem: its solution is NP-hard. Many algorithms and several heuristics, especially of greedy nature and evolutionary approaches, have been used to provide an approximate solution. To this problem, a new algorithm is proposed in this paper: particle swarm optimization (PSO). According to our experimental results, the solution achieved by the PSO algorithm showed a speed of execution, convergence capacity and consistence that allow electing it to use in data warehouse systems of medium dimensionalities.

关键词： data cube selection data warehousing discrete particle swarm algorithm OLAP multidimensional databases

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Distribution Network Reconstruction Based on discrete particle swarm algorithm Based on Quantum Coding

Distribution Network Reconstruction Based on Discrete Partic...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Luo, Ning Chen, Ludong Liu, Jinsen Zhang, Pengcheng Zheng, Fei Power Grid Planning and Research Center of Guizhou Power Grid Co. Ltd. Guizhou Guiyang China

ISBN: (纸本)9798350383348

Quantum-coded discrete particle swarm optimization (QBPSO) is a new optimization method which combines quantum computation principle and traditional particle swarm optimization (PSO) algorithm. By introducing the probabilistic representation of qubits, the algorithm improves the population diversity and enhances the global search ability. In the distribution network reconfiguration problem, the algorithm can be used to find the optimal structure of the network in order to reduce the loss, balance the load, improve the reliability of power supply and so on. © 2024 IEEE.

关键词： current overload theory discrete particle swarm algorithm Distribution network reconstruction quantum coding smart grid

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Test Frequency Selection of Analog Circuit Based on discrete particle swarm algorithm

Test Frequency Selection of Analog Circuit Based on Discrete...

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2012年计算机应用与系统建模国际会议

作者： Zheng Zhigang Wu Liang Army Aviation Research Institute

A method of optimal test frequency selection for analog circuit fault diagnosis is proposed. The method simulates the circuit faulty states only in the bands with highest fault sensitivity and that reduces part of simulation cost. Test frequency selection is considered as a multi-objective optimization problem, discrete particle swarm algorithm is used as a new approach to get optimal test frequency sets. An example shows that the proposed method is effective in frequency selection and works more efficiently compared with genetic algorithm.

关键词： Test frequency selection discrete particle swarm algorithm Analog circuit Fitness function

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Based on discrete particle swarm algorithm and Simulated Annealing algorithm to solve Course Timetabling Problem

Based on Discrete Particle Swarm Algorithm and Simulated Ann...

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2010 International Conference on Computer,Mechatronics, Control and Electronic Engineering (CMCE 2010)

作者： Kuancheng Zou Ye Qian School of Computer Science and Engineering Changchun University of Technology Changchun,China Xuan Liu Pengfei Zhang School of Computer Science and Engineering Changchun University of Technology Changchun,China

In this paper,course timetabling problem was referred as the research object,the objective function converted by many constraints and soft constraint condition was *** the mathematical model,proposed discrete particle swarm algorithm and simulated annealing algorithm that combined to settle course timetabling problem,which ensured that the particles escaped from the local minimum,guaranteed that the curriculum be more close to the ideal,the experimental results showed that the better results can be obtained by the method.

关键词： course timetabling problem discrete particle swarm algorithm simulated annealing algorithm

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A discrete shuffled frog optimization algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2011年第4期36卷 267-284页

作者： Baghmisheh, M. T. Vakil Madani, Katayoun Navarbaf, Alireza Univ Tabriz ICT Res Ctr Fac Elect & Comp Engn Tabriz Iran

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.

关键词： Optimization Binary genetic algorithm discrete shuffled frog algorithm discrete particle swarm algorithm

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Wind farm layout optimization based on 3D wake model and surrogate model

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2022年第9期19卷 956-966页

作者： Zhen, Zi Zhao, Wenrui Li, Shaojun East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ 130 Meilong Rd Shanghai 200237 Peoples R China

Wind farm layout optimization that considers the wake effect is crucial to improve the power generation and wind energy efficiency of a wind farm. In this study, a state-of-the-art three-dimensional (3D) wake model is used to optimize the layout of wind turbines (WTs) in a wind farm. A surrogate model based on a back propagation neural network(BPNN) is developed to simplify the complex process of calculating the wake deficits. Furthermore, discrete particle swarm algorithm is used for optimizing the wind farm layout while considering different hub heights. The results show that the surrogate model significantly reduces the computation time. The optimization of the layout of WTs with different hub heights in a wind farm substantially reduces the wake effect.

关键词： Wind farm layout optimization three-dimensional wake model surrogate model discrete particle swarm algorithm multiple hub heights

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Bi-Objective Optimal Scheduling With Raw Material's Shelf-Life Constraints in Unrelated Parallel Machines Production

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第11期50卷 4598-4610页

作者： Wang, Ming-Zheng Zhang, Ling-Ling Choi, Tsan-Ming Zhejiang Univ Sch Management Hangzhou 310058 Peoples R China Dalian Univ Technol Fac Management & Econ Inst Syst Engn Dalian 116024 Peoples R China Hong Kong Polytech Univ Fac Appl Sci & Text Inst Text & Clothing Hong Kong Peoples R China

This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimization of raw material costs. We formulate a bi-objective nonlinear 0-1 integer programming model for it. To solve the bi-objective problem which is NP-hard, we propose an evolutionary discrete particle swarm optimization algorithm (EDPSO) with a hybrid-greedy method (for generating the initial population), a new particle updating strategy, and an SPT-local search method (which has been proven for improving solutions' quality theoretically and practically), and the Pareto archive updating strategy for storing good solutions. Computational experiments verify the effectiveness of EDPSO and show that it can obtain better solutions compared to other competing algorithms based on four important performance metrics.

关键词： Raw materials Parallel machines Job shop scheduling Optimal scheduling Bi-objective optimization discrete particle swarm algorithm raw material's shelf life unrelated parallel machines scheduling

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Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 615-631页

作者： Guo, Yuming Jinggangshan Univ Coll Mech & Elect Engn Jian 343009 Jiangxi Peoples R China

The variant design node configuration is an effective method to solve a trade-off that exist in mass customization production between the requirements of product diversification and the product's cost and delivery time. However, configuration changes in a product development network may generate an avalanche effect of design change propagation, which can consume substantial design resources. In order to control design change propagations, an evaluation method based on network nodes importance for variant design node schemes is proposed for a product development network. Firstly, the evaluation indexes, including the betweenness, variant deign node set importance, and network clustering coefficient, are integrated to describe the network characteristics of the set of variant design nodes. Then, combining the time and resource constraints for the variant design, a discrete particle swarm algorithm is employed to optimize the configuration of the nodes. The configuration solution for the variant design nodes satisfies the need to control the variant design propagation. Compared with the established greedy algorithm, the discrete particle swarm optimization can achieve better optimization performance in terms of the algorithm's convergence and computation time. It is meaningful to understand the mechanism of product configuration change propagation in depth in order to choose the variant design nodes rationally and efficiently in complex product development networks. Lastly, an example of variant design for a type of cleaning robot product verifies the effectiveness of the proposed method.

关键词： Product development network Variant design Nodes configuration Nodes importance Network clustering coefficient discrete particle swarm algorithm

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