The Proposed Adaptive Acceleration Particle Swarm Optimization (AAPSO).
Therefore, we have proposed an adaptive acceleration particle swarm optimization (AAPSO) method that selects the acceleration coefficients using particle fitness values.
Utilizing the above equations, the velocity function acceleration coefficients are computed in AAPSO. The evaluation of the coefficients using the AAPSO equations enables the SVM to provide more accurate results.
Parameter Selection for SVM with AAPSO. To obtain more precise recognition, the SVM parameters are optimized using the AASPO method.
To accomplish the performance analysis, we performed 10 rounds of experiments using the AAPSO and PSO methods.
The performance of our proposed AAPSO and the standard PSO methods on these standard functions, in terms of their fitness values for different numbers of iterations, is shown in Figure 5.
The results in Figure 5 show that our proposed AAPSO method yields more accurate particles that have lower fitness values than those generated by the PSO method.
In Figure 6, the proposed AAPSO technique obtained more accurate particles that have minimum fitness values smaller than those obtained with the PSO method.
In 10 experiments, our proposed AAPSO method attained higher iris image classification accuracy than the standard PSO-SVM.
The two-day forum, to be held in the Red sea resort of Sharm El-Sheikh on May 15-16, will discuss means of dealing with the repercussions of the global financial crunch on the socio-economic development in NAM countries, the AAPSO
said in a press release on Thursday.