Empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization

The objective of this paper is to investigate whether the performance of the self-adaptive the parameters in 3PDE can improve the performance for function optimization. In this paper, we firstly propose three new algorithms (3PDE-SACr, 3PDE-SAF and 3PDE-SACrF). The preliminary testing is carried out to compare their performance with 3PDE to determine the best algorithm for the next step to self-adapt the population size. Here, the best algorithm from the preliminary testing will be chosen for the testing on self-adapting the population size in absolute and relative encodings. The preliminary testing showed that 3PDE-SAF performed the best for the first three proposed algorithms. So, 3PDE-SAF is chosen for the self-adaptive population size to test in absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) encodings and the final result showed that 3PDE-SAF-Rel performed slightly better than all the proposed algorithms in terms of its average performance and its stability.