Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM

2011 ◽  
Vol 38 (5) ◽  
pp. 4929-4934 ◽  
Author(s):  
Qi Wu
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Parameters Selection ◽  
Cauchy Mutation ◽  
Selection Of

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy

2014 ◽  
Vol 1049-1050 ◽  
pp. 1654-1657
Author(s):  
Jie Liu ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Parameters Selection ◽  
Sharing Strategy ◽  
Selection Of

To optimize the parameters of LS-SVM effectively, an improved Particle Swarm Optimization (PSO) algorithm is proposed to select the optimal parameters combination. For the improvement of the precocity in PSO algorithm, an multi-particles sharing strategy is introduced in simple PSO algorithm to enhance the convergence. The simulation indicates that the proposed PSO algorithm has a better selection on LS-SVM parameters.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

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