Optimal Value of Excitation of Self-excited Induction Generators by Simulated Annealing

2018 ◽  
pp. 171-178 ◽  
Author(s):  
Writwik Balow ◽  
Arabinda Das ◽  
Amarnath Sanyal ◽  
Raju Basak
Keyword(s):  
Simulated Annealing ◽  
Induction Generators ◽  
Optimal Value

Query Optimization Based on the Simulated Annealing and Particle Swarm Optimization

2012 ◽  
Vol 229-231 ◽  
pp. 1870-1873
Author(s):  
Ren Jie Song ◽  
Yan Wang
Keyword(s):  
Simulated Annealing ◽  
Query Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Optimal Value ◽  
Global Optimal ◽  
Annealing Algorithm

In order to allow the user to quickly and accurately search the required information, a query optimization method based on a simulated annealing and particle swarm hybrid algorithm is proposed. The basic idea is: the query population into two flat sub populations, a sub population by using simulated annealing algorithm optimization, another sub populations by using particle swarm algorithm optimization, comparison of two adaptive values, to find the global optimal value. The experimental results show that the mixed algorithm, can further improve the precision and recall of query optimization.

A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

2013 ◽  
Vol 325-326 ◽  
pp. 1628-1631 ◽  
Author(s):  
Hong Zhou ◽  
Ke Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Optimal Value ◽  
Swarm Algorithm

Be aimed at the problems that K-medoids algorithm is easy to fall into the local optimal value and basic particle swarm algorithm is easy to fall into the premature convergence, this paper joins the Simulated Annealing (SA) thought and proposes a novel K-medoids clustering algorithm based on Particle swarm optimization algorithm with simulated annealing. The new algorithm combines the quick optimization ability of particle swarm optimization algorithm and the probability of jumping property with SA, and maintains the characteristics that particle swarm algorithm is easy to realize, and improves the ability of the algorithm from local extreme value point. The experimental results show that the algorithm enhances the convergence speed and accuracy of the algorithm, and the clustering effect is better than the original k-medoids algorithm.

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Chairul Rahmadsyah Manik
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Production Scheduling ◽  
Production Control ◽  
Delivery Time ◽  
Production Schedule ◽  
A Company ◽  
Predetermined Time ◽  
Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan.   Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

Simulated annealing based optimal control for boundary layer transition

1999 ◽  
Author(s):  
W. MacCormack ◽  
O. R. Tutty ◽  
E. Rogers ◽  
P. A. Nelson
Keyword(s):  
Boundary Layer ◽  
Optimal Control ◽  
Simulated Annealing ◽  
Boundary Layer Transition ◽  
Layer Transition
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