scholarly journals Prediction of Evolution and Development Trend in Sports Industry Cluster Based on Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rui Cong ◽  
Hailong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Development Trend ◽  
Industrial Clusters ◽  
Economic Benefits ◽  
Local Extremum ◽  
Industry Cluster ◽  
Swarm Optimization ◽  
Sports Industry

Sports industry cluster refers to the economic phenomenon that sports related enterprises gather in a large number in a specific area. For the sports enterprises in the cluster, they can obtain huge competitive advantages through enterprise agglomeration, thus obtaining better development and rich economic benefits. The optimization of particle swarm optimization is interlinked with the agglomeration of industrial clusters. Therefore, in view of the limitation of the standard particle swarm optimization (PSO) algorithm, an improved particle swarm optimization algorithm-diaphragm particle swarm optimization (D-PSO) was proposed and used to simulate the formation of sports industry clusters. D-PSO introduces the cell membrane processing mechanism of the biological system into the PSO algorithm, which improves the ability of the PSO algorithm to get rid of local extremum points. The competitiveness value of the sports industry cluster is the value of the objective function solved by the D-PSO algorithm. The geographical coordinates of the industrial cluster were the locations in the particle search space of the D-PSO algorithm. The D-PSO algorithm is used to simulate the aggregation process of enterprises in the cluster. Compared with the standard PSO, the D-PSO algorithm has better convergence performance and optimal rate. The results of case analysis show that the proposed method can effectively predict the development trend of sports industrial clusters.

scholarly journals Forecasting Initial Public Offering Pricing Using Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) In Iran

2017 ◽  
Vol 7 (1) ◽  
pp. 336 ◽  
Author(s):  
Shaho Heidari Gandoman ◽  
Navab Kiamehr ◽  
Mahmood Hemetfar
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Initial Public Offering ◽  
Stock Exchange ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Benefits ◽  
Support Vector ◽  
Swarm Optimization ◽  
Public Offering

The present study compares the ability of neural networks, support vector machine, and model derived from combining particles swarm optimization (PSO) algorithm and support vector machine (SVM) to forecast the initial public offering pricing. The purpose of this research is to design a model that helps investors recognize the validity of the initial public offering pricing and hunt profitable opportunities. The variables used in this study are selected among those variables which are in the disposal of investors who have limited access to information before the offering. On the other hand, these results can be useful for publishing companies, admissions consultant, underwriting and legislators of the stock exchange. We have considered the ninth day offering prices, since volatilities are gone and prices seem to be more realistic. The results show that the combination of particle swarm optimization (PSO) algorithm and support vector machine (SVM) markedly increases the forecasting power. As a result, support vector machine models can increase the accuracy of initial public offering pricing and provide significant economic benefits as reducing less than real pricing costs.

scholarly journals Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dawei Gao ◽  
Xiangyang Li ◽  
Haifeng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Extremum ◽  
Local Optimum ◽  
Global Optimality ◽  
Critical Position ◽  
Swarm Optimization ◽  
Improved Pso

In the optimization design process, particle swarm optimization (PSO) is limited by its slow convergence, low precision, and tendency to easily fall into the local extremum. These limitations make degradation inevitable in the evolution process and cause failure of finding the global optimum results. In this paper, based on chaos idea, the PSO algorithm is improved by adaptively adjusting parameters r1 and r2. The improved PSO is verified by four standard mathematical test functions. The results prove that the improved algorithm exhibits excellent convergence speed, global search ability, and stability in the optimization process, which jumps out of the local optimum and achieves global optimality due to the randomness, regularity, and ergodicity of chaotic thought. At last, the improved PSO algorithm is applied to vehicle crash research and is used to carry out the multiobjective optimization based on an approximate model. Compared with the results before the improvement, the improved PSO algorithm is remarkable in the collision index, which includes vehicle acceleration, critical position intrusion, and vehicle mass. In summary, the improved PSO algorithm has excellent optimization effects on vehicle collision.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

scholarly journals Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

2021 ◽  
Vol 13 (6) ◽  
pp. 1207
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Yuming Jiang ◽  
Liying Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Geographic Location ◽  
Doa Estimation ◽  
Pso Algorithm ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Location Method ◽  
Esprit Algorithm ◽  
To Receive

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

2021 ◽  
Vol 40 (5) ◽  
pp. 9007-9019
Author(s):  
Jyotirmayee Subudhi ◽  
P. Indumathi
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Multiple Access ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Allocation Algorithm ◽  
User Clustering ◽  
Sum Rate

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

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