Developing a Decision Model Based on Decision Tree and Particle Swarm Optimization Algorithms to Identify Optimal Locations for Solar Power Plants Construction in Iran

2021 ◽  
Author(s):  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samani ◽  
Foad Minaei ◽  
Hamzeh Karimi Firozjaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Decision Model ◽  
Power Plants ◽  
Solar Power ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Based ◽  
Solar Power Plants

scholarly journals PENERAPAN PARTICLE SWARM OPTIMIZATION PADA ALGORITMA C 4.5 UNTUK SELEKSI PENERIMAAN KARYAWAN

2018 ◽  
Vol 4 (2) ◽  
pp. 51-59
Author(s):  
Agus Wiyatno
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Classification Tree ◽  
Particle Swarm ◽  
Classification Model ◽  
Accurate Information ◽  
Swarm Optimization ◽  
Organization Model ◽  
Goods And Services ◽  
Model Based

The Employees are the most vital element of the company as they had a big contribution and involved almost for all section on how the company will go up and down. Employees and the company affect the efficiency, effectiveness,designing, producing goods and services, oversee the quality, market products, allocating financial resources, and determines the overall goals and strategies of the organization. Therefore, organizations need accurate information and sustainable in order to get suitable candidates with the qualifications of the organization. Model algorithms are widely used in research related to the employee is C4.5 decision tree classification model. Advantages of using adecision tree classification models are the result of the decision tree is simple and easy to understand. Many studies using the method of decision tree and classification tree in predicting the employees selection but results theaccuracy of the resulting value is less accurate. In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accuratevalue of accuracy. After testing C 4.5 algorithm model based on particle swarm optimization, Implementation of particle swarm optimization can produce accuracy value of C 4.5 algorithm model from 80.80 % to 85.20 % and theAUC value from 0.878 to 0.891. By the formation the model selection of employees, the company can be helped for employee selection.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

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