Euclidean Distance Based Particle Swarm Optimization

2018 ◽  
pp. 115-124
Author(s):  
Ankit Agrawal ◽  
Sarsij Tripathi
Keyword(s):  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Swarm Optimization

Power Transmission and Transformation Project Establishing and Decision-Making Based on ASU-MOPSO Algorithm

2014 ◽  
Vol 521 ◽  
pp. 521-529 ◽  
Author(s):  
Ke Sun ◽  
Kai Xu ◽  
Zhao Ming Zheng ◽  
Xiao Yu Ding ◽  
Ke Sun ◽  
...  
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Power Transmission ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Computing Complexity ◽  
Crowding Distance ◽  
Decision Making Problem

This paper demonstrates an asynchronous-stepwise updated strategy multi-objective particle swarm optimization (ASU-MOPSO) algorithm to improve the convergence and diversity of the multi-objective particle swarm optimization. In the process of the elite reduction, we utilize the asynchronous grid strategy to filter particles since this strategy has lower computing complexity. Meanwhile, the stepwised Euclidean crowding distance strategy is presented to filter particles within every grid, which uses the sum of the Euclidean distance with the two nearest particles to replace of the traditional crowding distance. This strategy can avoid the destruction of distribution diversity. Finally, our algorithm is successfully applied for the actual power transmission and transformation project establishing and decision-making problem. Comparing with traditional MOPSO based on crowding distance strategy and grid strategy, our algorithm can obtain the better solution.

scholarly journals A Modified Particle Swarm Optimization with Neural Network via Euclidean Distance

2018 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Asia L. Jabar ◽  
Tarik A. Rashid
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Classification Problem ◽  
Feed Forward Neural Network ◽  
Distance Method ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Hidden Layer

<p>In this paper, a new modified model of Feed Forward Neural Network with Particle Swarm Optimization via using Euclidean Distance method (FNNPSOED) is used to better handle a classification problem of the employee’s behavior. The Particle Swarm Optimization (PSO) as a natural inspired algorithm is used to support the Feed Forward Neural Network (FNN) with one hidden layer in obtaining the optimum weights and biases using different hidden layer neurons numbers. The key reason of using ED with PSO is to take the distance between each two-feature value then use this distance as a random number in the velocity equation for the velocity value in the PSO algorithm. The FNNPSOED is used to classify employees’ behavior using 29 unique features. The FNNPSOED is evaluated against the Feed Forward Neural Network with Particle Swarm Optimization (FNNPSO). The FNNPSOED produced satisfactory results.</p>

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

2021 ◽  
pp. 014233122198900
Author(s):  
Jingyi Lu ◽  
Xue Qu ◽  
Dongmei Wang ◽  
Jikang Yue ◽  
Lijuan Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Noise Reduction ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Signal Filtering ◽  
Mode Decomposition ◽  
Mean And Variance ◽  
The Mean

In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and [Formula: see text] of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering.

Ergonomics Layout Optimization of the Aircraft Cockpit Based on Particle Swarm Optimization

2014 ◽  
Vol 1044-1045 ◽  
pp. 1851-1854
Author(s):  
Xin Liu ◽  
Sui Huai Yu ◽  
Tian Cheng Gong ◽  
Qing Zhang ◽  
Ming Lei Zhao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Layout Design ◽  
Geometric Constraints ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Minimum Euclidean Distance ◽  
Proper Values

The problem of neglecting ergonomic factors on the aircraft cockpit layout design which leads to the pilots feeling tired very soon should be solved imminently. Since there are a lot of ergonomic constraints while there are hardly any algorithms to solve the problem, the particle swarm optimization is mentioned. Firstly, ergonomic geometric constraints and ergonomic space constraints are confirmed. Secondly, the objective function is confirmed based on the minimum Euclidean distance. Thirdly, to avoid local optimum, particle swarm optimization is used to find the best coordinate values of facilities. At last, the best values of different facilities are needed to be compromised by each other to confirm the most proper values for every facility and form the optimal scheme of designing the layout of aircraft cockpits.

scholarly journals PARTICLE SWARM OPTIMIZATION PADA METODE KNN EUCLIDEAN DISTANCE BERBASIS VARIASI JARAK UNTUK PENILAIAN

2018 ◽  
Vol 9 (1) ◽  
pp. 59
Author(s):  
Herry Adi Chandra
Keyword(s):  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
K Nearest Neighbor ◽  
Swarm Optimization

Penilaian kinerja terhadap Lembaga kursus merupakan salah satu pola penguatan kelembagaan yang dilakukan oleh Direktorat pembinaan kursus dan pelatihan dengan berbagai parameter.Akan tetapi system penilaian akreditasi masih memakai    cara yg sangat manual, dengan melakukan penilaian  ke lembaga kursus dan pelatihan .oleh karena itu tugas pokok dan fungsi Badan Akreditasi Nasional Pendidikan Non Formal (BAN-PNF) adalah melaksanakan akreditasi terhadap Lembaga Kursus dan Pelatihan (LKP). Akreditasi adalah kegiatan penilaian kelayakan satuan beserta program PNF berdasarkan atas kriteria yang telah ditetapkan. Untuk menilai kelayakan tersebut disusun instrumen akreditasi yang mengacu pada Standar Nasional Pendidikan (SNP) sebagaimana ditetapkan melalui Peraturan Pemerintah Republik Indonesia Nomor 32 Tahun 2013 tentang Perubahan atas Peraturan Pemerintah Republik Indonesia Nomor 19 Tahun 2005, yang mencakup delapan    standar. Dengan menggunakan metode KNN dan optimasi Particle Swarm Optimization untuk mningkatkan akurasi dengan jumlah data yg sangat bnyak hasil penelitian juga mendapatkan tingkat akurasi yang optimal pada rumus variasi jarak yaitu K- Nearest neighbor Euclidean distance dari 96,60 %, sampai 99,69 %. Dapat dilihat jelas adanya kenaikan yang dihasilkan, walaupun tidak terlalu besar yaitu hanya sebesar 3,09 %.jadi untuk   meningkatkan hasil akurasi yang optimal maka K-NN Euclidean distance   di optimasi dengan Particle Swarm Optimization, yang mampu meningkatkan nilai akurasi sehingga dapat menjadi acuan yang sangat baik untuk klasifikasi akreditasi Lembaga kursus. Kata Kunci :  K-NN, Euclidean Distance, Particle Swarm Optimization

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