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Research on the collaborative mechanism between site selection optimization model and control of green space planning under the background of urban renewal

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DISCOVER SUSTAINABILITY 2025年第1期6卷 1-22页

作者： Li, Xiangyun Dai, Desheng Dong, Jingwen Hu, Kaili Nanjing Municipal Design & Res Inst Co Ltd Nanjing 210018 Peoples R China Nanjing Inst Technol Sch Civil Engn & Architecture Nanjing 211167 Peoples R China Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210024 Peoples R China

As a part of integrated urban ecosystems, green space is an important target of urban renewal planning. In the process of urban renewal of green space, it is difficult to control the special planning for green space systems, and the scale of urban public space is insufficient. From the perspective of spatial governance, it is of great practical importance to study the relationship between site selection optimization and the control of special planning for green space systems. This paper proposes a method of green space evaluation and site selection optimization based on Geographic Information System (GIS) modelling and particle swarm optimization algorithm. It optimizes the green space allocation by using the model constraints. Taking the special planning for green space systems in Gaochun District, Nanjing City as an example, it analyzes the constraints of the special planning for green space systems in the site selection optimization model from two perspectives: indicator conditional constraints and space requirement constraints. The GIS model of optimal site selection is introduced into the special planning for green space systems' adjustment process in the context of urban renewal. This approach can bring a variety of positive benefits to meet the needs of green space sharing and people's well-being.

关键词： Green space planning Site selection optimization Urban renewal particle swarm optimization algorithm

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Gene Regulatory Network Construction Based on a particle swarm optimization of a Long Short-term Memory Network

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CURRENT BIOINFORMATICS 2020年第7期15卷 713-724页

作者： Tang, Zhenhao Chai, Xiangying Wang, Yu Cao, Shengxian Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China

Background: The Gene Regulatory Network (GRN) is a model for studying the function and behavior of genes by treating the genome as a whole, which can reveal the gene expression mechanism. However, due to the dynamics, nonlinearity, and complexity of gene expression data, it is a challenging task to construct a GRN precisely. And in the circulating cooling water system, the Slime-Forming Bacteria (SFB) is one of the bacteria that helps to form dirt. In order to explore the microbial fouling mechanism of SFB, constructing a GRN for the fouling-forming genes of SFB is significant. Objective: Propose an effective GRN construction method and construct a GRN for the fouling-forming genes of SFB. Methods: In this paper, a combination method of Long Short-Term Memory Network (LSTM) and Mean Impact Value (MIV) was applied for GRN reconstruction. Firstly, LSTM was employed to establish a gene expression prediction model. To improve the performance of LSTM, a particle swarm optimization (PSO) was introduced to optimize the weight and learning rate. Then, the MIV was used to infer the regulation among genes. In view of the fouling-forming problem of SFB, we have designed electromagnetic field experiments and transcriptome sequencing experiments to locate the fouling-forming genes and obtain gene expression data. Result: In order to test the proposed approach, the proposed method was applied to three datasets: a simulated dataset and two real biology datasets. By comparing with other methods, the experimental results indicate that the proposed method has higher modeling accuracy and it can be used to effectively construct a GRN. And at last, a GRN for fouling-forming genes of SFB was constructed using the proposed approach. Conclusion: The experiments indicated that the proposed approach can reconstruct a GRN precisely, and compared with other approaches, the proposed approach performs better in extracting the regulations among genes.

关键词： Gene regulatory network long short-term memory particle swarm optimization algorithm mean impact value slime-forming bacteria yeast gene data machine learning

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Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based optimization and the Whale optimization algorithms

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SN Computer Science 2024年第4期5卷 417页

作者： Nabahat, Mehrdad Khiyabani, Farzin Modarres Navmipour, Nima Jafari Department of Mathematics Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran

Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are opti

关键词： Bilateral filter Gaining sharing knowledge-based optimization algorithm Generalized Cauchy filter Image de-noising Image noise particle swarm optimization algorithm Whale optimization algorithm

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A Improved particle swarm optimization algorithm for the Application with Boundary-layer Problems of Power-law Fluids

A Improved Particle Swarm Optimization Algorithm for the App...

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2013 International Conference on Mechanical Engineering and Instrumentation(ICMEI 2013)

作者： Jianhui Zhou Lixi Yue Yannan Lu College of Qian'an Hebei United University

In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm(ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Ordinary differential equations Parameter estimation boundary-layer problems

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A Design Method of Biplanar Uniform Field Coils Based on Magnetic Shielding Rooms 21

A Design Method of Biplanar Uniform Field Coils Based on Mag...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Yuan, Shuai Shi, Minxia Zhang, Leran Yang, Jianzhi Li, Teng Ma, Yuzheng Beihang Univ Sch Instrumentat & Optoelect Engn Minist Educ Key Lab Ultra Weak Magnet Field Measurement Techn Beijing Peoples R China

ISBN: (纸本)9798350348958;9798350348965

The digest presents a design method of biplanar coils to realize precise compensation for residual magnetic fields inside magnetic shielding rooms (MSR). The magnetic field generated by the biplanar coils inevitably couples with the magnetic shielding material, thereby deteriorating the uniformity of the magnetic field in the target region. Based on the coupling principle of the coils, the mirror image method is introduced into the traditional target field method. An improved particle swarm optimization (PSO) algorithm is used to obtain the coils' parameters, thus determining the shapes of the coils. The simulation results show that the B-x coil designed by this method can generate a more uniform magnetic field inside MSR.

关键词： biplanar coils magnetic shielding rooms magnetic shielding material target field method particle swarm optimization algorithm

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Two-Stage Adaptive Constrained particle swarm optimization Based on Bi-Objective Method

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IEEE ACCESS 2020年 8卷 150647-150664页

作者： Feng, Qian Li, Qing Wang, Heng Feng, Yongfeng Pan, Yichen Univ Sci & Technol Sch Automat & Elect Engn Beijing 100083 Peoples R China North China Univ Sci & Technol Coll Mech Engn Tangshan 063009 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability.

关键词： optimization particle swarm optimization Convergence Sociology Statistics Linear programming Heuristic algorithms Constrained optimization particle swarm optimization algorithm bi-objective optimization adaptive

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Multi-objective optimization of printed circuit heat exchanger with airfoil fins based on the improved PSO-BP neural network and the NSGA-II algorithm

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第6期55卷 2125-2138页

作者： Wang, Jiabing Zeng, Linlang Yang, Kun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objec-tive functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Multi -objective optimization Printed circuit heat exchanger Airfoil fin Design of experiment BP neural network particle swarm optimization algorithm

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Design of BLDC Motor Using African Vulture optimization algorithm Based Trained FNN

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SN Computer Science 2024年第4期5卷 385页

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India

Brush-less DC motors with permanent magnets are widely used in robotics, electric vehicles, and other industrial applications. Enhancing the performance of a BLDC motor can indeed be challenging due to the presence of non-linearities and complex design considerations. The present research aims to increase the effectiveness and performance of BLDC motors by using a feed-forward neural network (FNN). It is possible to quickly determine the FNN’s appropriate weight and bias settings by using the cutting-edge African Vulture Optimisation algorithm (AVOA). Combining biological inspiration, machine learning, and motor technology, it develops a distinctive and promising way to improve the performance of BLDC motors. This study also includes the sensitive analysis of controlling parameters of the AVOA to investigate its effect on the statistical variables of the fitness function. This aids in optimizing the algorithm for better outcomes. The proposed method is then contrasted with various FNN learning algorithms based on Practical swarm optimization (PSO), Gravitational Search algorithm (GSA), and PSO-GSA. Considerations like convergence rate and the capacity to steer clear of local minima are probably evaluated throughout the comparison. According to the results, AVOA-based optimization performs better than the other methods (PSO, GSA, and PSO-GSA) in terms of convergence speed and its ability to avoid becoming stuck in local minima. This suggests that the AVOA is useful for training FNNs to optimize the design parameters of BLDC motors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Brush-less DC motors Feed-forward neural network Gravitational Search algorithm particle swarm optimization algorithm

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optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2020年第1期42卷 1-14页

作者： Saffaran, Ali Moghaddam, Masoud Azadi Kolahan, Farhad Khayyam Univ Mashhad Dept Mech Engn Mashhad Razavi Khorasan Iran Ferdowsi Univ Mashhad Dept Mech Engn Mashhad Razavi Khorasan Iran

In the present study, artificial neural network (ANN) along with heuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA), has been employed to carry out the modeling and optimization procedure of electrical discharge machining (EDM) process on AISI2312 hot worked steel parts. Surface roughness (SR), tool wear rate (TWR) and material removal rate (MRR) are the process quality measures considered as process output characteristics. Determination of a process variables (pulse on and off time, current, voltage and duty factor) combination to minimize TWR and SR and maximize MRR independently (as single objective) and also simultaneously (as multi-criteria) optimization is the main objective of this study. The experimental data are gathered using Taguchi L-36 orthogonal array based on design of experiments approach. Next, the output measures are used to develop the ANN model. Furthermore, the architecture of the ANN has been modified using PSO algorithm. At the last step, in order to determine the best set of process output variables values for a desired set of process quality measures, the developed ANN model is embedded into proposed heuristic algorithms (SA and PSO) with which their derived results have been compared. It is evident that the proposed optimization procedure is quite efficient in modeling (with less than 1% error) and optimization (less than 4 and 7 percent error for single- and multi-objective optimizations, respectively) of EDM process variables.

关键词： Electrical discharge machining Taguchi technique Design of experiments Artificial neural network Simulated annealing algorithm particle swarm optimization algorithm

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Color quantization with particle swarm optimization and artificial ants

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SOFT COMPUTING 2020年第6期24卷 4545-4573页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Av Requejo 33 Zamora 49022 Spain

This article describes a color quantization algorithm that combines two swarm-based methods: particle swarm optimization and artificial ants. The proposed method is based on a previous method that solves the quantization problem by combining the particle swarm optimization algorithm with the K-means algorithm. K-means is a popular clustering method that has been applied to solve a variety of problems, including the color quantization problem. Nevertheless, it is a time-consuming method, which makes combining the particle swarm optimization algorithm and K-means less suitable than other color quantization techniques. The proposed method, however, discards the K-means algorithm and applies the Ant-tree for color quantization algorithm in order to reduce execution time. This article shows that the new method outperforms the original one, since it requires less time to obtain higher quality images. In addition, the images produced are also of better quality than those produced by other well-known color quantization methods, such as Neuquant, Octree, Median-cut, Variance-based, Binary splitting and Wu's methods.

关键词： Color quantization Artificial ants Ant-tree algorithm particle swarm optimization algorithm Clustering

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