Adaptive simulated annealing genetic algorithm for control applications

Abstract

We propose an efficient hybrid genetic algorithm named the adaptive simulated annealing genetic algorithm (ASAGA) which is used in control applications. Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, combining them produces an adaptive algorithm that has the merits of both genetic algorithms and simulated annealing by introducing an adaptive cooling schedule and mutation operator such as simulated annealing. The validity and efficiency of the proposed algorithm are illustrated by simulation examples for system identification and control that include neural networks which are particularly suitable for applications of ASAGA.