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4th International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM)

作者： Chen Ailing Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9783038352884

Partner selection decisions are an important component of production and supply chain management. However, partner selection is a typical multi-criteria decision-making problem and many factors affect partner selection;it is hard to select the most appropriate partners in real circumstances, especially in agricultural product supply chain. In this study, a discrete particle swarm optimization algorithm is proposed to solve the problem. A numerical example for partner selection of agricultural product supply chain is given to illustrate the application of the proposed method and shows the proposed method is feasible and effective.

关键词： Partner selection agricultural product supply chain particle swarm optimization algorithm

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Space-time Simulation Model Based on particle swarm optimization algorithm for Stadium Evacuation

Space-time Simulation Model Based on Particle Swarm Optimiza...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zong, Xinlu Xiong, Shengwu Xu, Hui Duan, Pengfei Hubei Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9781479914883

In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are considered and the corresponding moving rules are defined. The model is applied to a stadium and simulations are carried out to analyze the space-time evacuation efficiency by different behaviors. The simulation results show that the behaviors of going with the crowd and panic will slow down the evacuation process while quickest evacuation psychology can accelerate the process, and panic is helpful to some extent. The setting of parameters is discussed to obtain best performance. The simulation results can offer effective suggestions for evacuees under emergency situation.

关键词： stadium evacuation particle swarm optimization algorithm simulation component

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An Improved particle swarm optimization algorithm

An Improved Particle Swarm Optimization Algorithm

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International Forum on Materials Analysis and Testing Technology (IFMATT 2013)

作者： Ni, Hongmei Wang, Weigang Northeast Petr Univ Comp & Informat Technol Coll Daqing Peoples R China Northeast Petr Univ Mech Sci & Engn Coll Daqing Peoples R China

ISBN: (纸本)9783037859537

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.

关键词： particle swarm optimization algorithm Multi-peak function optimization Niche

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A Modified particle swarm optimization algorithm for Dynamic Multiresponse optimization Based on Goal Programming Approach 21

A Modified Particle Swarm Optimization Algorithm for Dynamic...

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21st International Conference on Management Science & Engineering (ICMSE)

作者： Zhang Liu-yang Ma Yi-zhong Zhu Lian-yan Wang Jian-jun Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781479953769

While many of the previous applications based on the Taguchi method only focus on single-response optimization in static system, dynamic multiresponse optimization has received only limited attentions. optimization of dynamic multiresponse aims at finding out a setting combination of input controllable factors that will result in optimal solutions for all response variables at each signal level. However, it is often difficult to find an optimal setting when multiple responses are simultaneously considered because of their contradiction among the requirements. Hence, a new robust design optimization procedure based on response surface methodology is proposed in the article. The polynomial models of system sensitivity and the error variance for each response are firstly fitted, and corresponding individual desirability functions based on their respective characteristic are defined. Then, goal programming approach is used to resolve multiresponse optimization problems. Because the problems are often multiobjective optimization problems and are often with multipeak distribution, multiconstraint and high nonlinearity, traditional gradient algorithms are easy to obtain local optimal solutions. So a modified particle swarm optimization algorithm is proposed to search global optimal solution. The example shows that the proposed approach can obtain more effectively solutions for dynamic multiresponse optimization problems.

关键词： dynamic multiresponse multiobjective optimization goal programming response surface methodology particle swarm optimization algorithm

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Process Parameters optimization of Plasma Spraying Nanostructured Coating Based on particle swarm optimization algorithm

Process Parameters Optimization of Plasma Spraying Nanostruc...

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International Conference on Application of Materials Science and Environmental Materials (AMSEM)

作者： Yang, Bin Tianjin Univ Technol & Educ Sch Automobile & Transportat Tianjin 300222 Peoples R China

ISBN: (纸本)9783038352709

Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were optimized based on particle swarm optimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO algorithm. A BP neural network model was built. Process parameters of coating were optimized based on PSO algorithm. The results shown that maximal bonding strength was 33.08MPa. Process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating were obtained. The results were superior to design of orthogonal optimization. It provided definite reference for selecting the best process parameters of plasma spraying nanostructured Al2O3-13% TiO2 (mass fraction) coating.

关键词： Plasma Spraying Nanostructured Coating BP Neural Network particle swarm optimization algorithm Process Parameters optimization

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Multi-objective Reactive Power optimization Based on Refined Chaos particle swarm optimization algorithm

Multi-objective Reactive Power Optimization Based on Refined...

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International Symposium on Vehicle, Mechanical, and Electrical Engineering (ISVMEE 2013)

作者： Ai, Ying Nie, Hongwei Su, Yixin Zhang, Danhong Peng, Yao Wuhan Univ Technol Dept Automat Wuhan 430070 Peoples R China China Nucl Power Operat Technol Corp Wuhan 430070 Peoples R China

ISBN: (纸本)9783038350033

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value;In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particle's fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

关键词： Reactive Power optimization Cube Map particle swarm optimization algorithm Dynamic Inertia Weight

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Pricing and Replenishment Decisions for Vegetable Commodities Based on ARIMA Prediction and particle swarm optimization algorithms

Pricing and Replenishment Decisions for Vegetable Commoditie...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wang, Xiaodi Hu, Zhenshuo Wang, Zhaokun Ludong University Yantai China

ISBN: (纸本)9798350309614

Supermarkets need to make replenishment decisions for the vegetable category of the day without knowing exactly what the specific individual items and the price of the stock are. Accurate market demand analysis is crucial for replenishment and pricing decisions. In this paper, for the problems in automatic pricing and replenishment of vegetables, based on the idea of cost-plus pricing and linear programming, a model is established by determining the indicators of sales volume, selling price, and relevance of vegetables, with the goal of finding the relevance of each part and obtaining the maximum revenue, and the model is solved using the particle swarm algorithm. Firstly, considering the different selling prices and sales volumes of vegetable categories in different time periods, two-by-two correlation analysis is carried out by calculating the Pearson correlation coefficient, and the correlation heat map is used to reflect the correlation problem between each vegetable category. Then, the ARIMA model is established to predict the cost of each vegetable category, and the quadratic programming method is used to predict the total daily replenishment and pricing strategy, which gives the replenishment volume and pricing strategy of each category with the greatest benefit in the coming week. Finally, by establishing a planning model for single product selection and using particle swarm algorithm to solve the problem, different profit margins are chosen to calculate different single product pricing, and the replenishment quantity and pricing strategy for single product with the maximum revenue are formulated. The proposed model is fully integrated with reality and has the advantages of simplicity and easy to understand. © 2023 IEEE.

关键词： ARIMA analysis correlation coefficient linear programming particle swarm optimization algorithm

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FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第12期51卷 2519-2539页

作者： Javanmardi, Saeed Shojafar, Mohammad Persico, Valerio Pescape, Antonio Univ Napoli Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy Univ Surrey 5G Innovat Ctr Inst Commun Syst Guildford Surrey England

In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particle swarm optimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.

关键词： fog computing Internet of Things Mamdani fuzzy inference system particle swarm optimization algorithm resource management task scheduling

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A Modified particle swarm optimization algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor

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JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME 2014年第2期136卷 021701-021701页

作者： Chang, S. H. Jeng, Y. R. Natl Chung Cheng Univ Dept Mech Engn Adv Inst Mfg High Tech Innovat Chiayi 62102 Taiwan

The performance of an aerostatic bearing with a pocketed orifice-type restrictor is affected by the bearing size, pocket size, orifice design, supply pressure, and bearing load. This study proposes a modified particle swarm optimization (MPSO) algorithm to optimize a double-pad aerostatic bearing. In bearing optimization, the upper and lower bearing designs are independent and several design variables that affect bearing performance must be considered. This study also applies the concept of mutation from a genetic algorithm. The results show that the MPSO algorithm has a global search capability and high efficiency to optimize a problem with several design variables and that the mutation can provide an avenue for particles to escape from a local optimal value.

关键词： aerostatic bearing particle swarm optimization algorithm orifice-type restrictor

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optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm

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PROGRESS IN ADDITIVE MANUFACTURING 2021年第2期6卷 199-215页

作者： Shirmohammadi, Mohammad Goushchi, Saeid Jafarzadeh Keshtiban, Peyman Mashhadi Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Urmia Univ Technol Fac Mech Engn POB 57155-419 Orumiyeh Iran

Due to the significant impact on the product quality and performance, the surface roughness of produced parts by 3D printers is one of the important factors in the 3D printing process. Then, the main objective of this research is to determine the optimal composition of input parameters to minimize surface roughness, using hybrid artificial neural network and particle swarm algorithm. For this purpose, after using Central Composite Design (CCD) of experiments with five independent parameters (nozzle temperature, layers thickness, printing speed, nozzle diameter and material density) with three levels, 43 flat parts were produced with a three-dimensional printer, and roughness tests were performed on produced parts. After training experimental matrix by multilayer perceptron neural network (7-4-1) with a coefficient of 0.95, the subjected matrix was combined with the particle swarm algorithm to determine the optimal composition of input parameters. To verify results accuracy, the optimized process parameters obtained from the combined algorithm, have been tested with experimental results. In addition, to specify the effect of input parameters on the surface roughness, a quadratic model has been developed using Response Surface Method (RSM). Based on results of the hybrid algorithm, the optimal combination of input parameters was extracted. It was inferred that, the nozzle temperature of 192.20 degrees C, the layers' thickness of 100 mu m, the printing speed of 97.06 mm/s, the nozzle diameter of 0.3 mm and the internal density of 24.88% lead to the surface roughness of 11.319. Therefore, the use of this hybrid algorithm improves the surface quality of the printed parts during the 3D printing process.

关键词： 3D printing Surface roughness Artificial neural network particle swarm optimization algorithm Response surface method

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