scholarly journals Visitor Forecasting Wisata Bahari Lamongan (WBL) Using Hybrid Particle Swarm Optimization (PSO) and Seasonal ARIMA

2021 ◽  
Vol 1 (2) ◽  
pp. 27-35
Author(s):  
Dinita Rahmalia
Keyword(s):  
Particle Swarm Optimization ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mean Square ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Tourism Sector ◽  
Simulation Results

The revenue of city is determined by some factors, one of them is tourism sector. A problem of tourism sector is forecasting visitors Wisata Bahari Lamongan (WBL). Because data of the number of visitors WBL are fluctuating and seasonal, then it is required Seasonal ARIMA method. In the Seasonal ARIMA method, there are some parameters that should be optimized for producing forecasting with small mean square error (MSE). In this research, Seasonal ARIMA parameters will be optimized by Particle Swarm Optimization (PSO). PSO is optimization algorithm inspired by behavior of birds group in searching food. Based on simulation results, PSO algorithm can optimize Seasonal ARIMA parameter which is optimal and it can produce forecasting result with small MSE.

A New Improved Adaptive Hybrid Particle Swarm Optimization Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1710-1713
Author(s):  
Xiang Tian ◽  
Yue Lin Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Searching Ability ◽  
Learning Factors

This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and aims at the shortcoming of PSO algorithm, which is easily plunging into the local minimum, then we proposes a new improved adaptive hybrid particle swarm optimization algorithm. It adopts dynamically changing inertia weight and variable learning factors, which is based on the mechanism of natural selection. The numerical results of classical functions illustrate that this hybrid algorithm improves global searching ability and the success rate.

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.

Analysis of FBG Reflection Spectrum with PSO Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1953-1957
Author(s):  
Zhao Xia Wu ◽  
Shu Qiang Chen ◽  
Jun Wei Wang ◽  
Li Fu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
High Precision Measurement ◽  
Swarm Optimization ◽  
Fbg Sensor ◽  
Fast Convergence Rate ◽  
Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

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