Global optimization method using intermittency chaos

The nonlinear global optimization methods developed so far can be divided into deterministic methods and stochastic methods. Various approaches proposed recently in which chaos, which is a probabilistic phenomenon and is generated by a deterministic dynamical system, is used to solve nonlinear global optimization problems. Chaos is used in these methods because fluctuations away from the local solution and global optimal solution can be searched without trapping into the local solution. The method of unconstrained global optimization proposed here uses intermittency chaos. In this proposed method, the time history of a discrete dynamical system containing the return map, which generates the intermittency chaos, is simulated and during convergence (nonperiodically) to the local optimal solution, the global optimal solution is searched without trapping into the local optimal solution. The validity of the proposed method is confirmed by applying it to a standard test problem.