Abstract:
In this work we presented the performances of a new class of evolutionary
algorithms called chaotic optimization algorithm (COA). Proposed to solve
nonlinear optimization problems with bounded variables by Caponetto et al.
Chaotic optimization is a new stochastic optimization algorithm, which directly
uses chaotic variables to find the optimal solution. Different chaotic maps have
been considered, combined with several working strategies. We propose five
different 2D chaotic maps in an optimization algorithm using a two-step chaotic optimization method and compare them. This study reviews and compares
chaotic optimization algorithms from the literature. Moreover, the two-phase
strategy is a commonly used technique in a COA to refine the solution and
help escape local optima. A performance study is conducted to understand
their impact on a chaotic optimization algorithm.
