An evolutionary framework using particle swarm optimization for classification method PROAFTN

Abstract

The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) 
algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient 
evolutionary optimization algorithm using the social behavior of living organisms to explore the search 
space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions 
to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To 
apply PROAFTN, the values of several parameters need to be determined prior to classification, such as 
boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes 
PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness 
of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain 
datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate 
that PSOPRO is very competitive with respect to the most common classification algorithms. Crown 
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