Asynchronous Differential Evolution with Restart

Asynchronous Differential Evolution ADE [1] is a derivative-free method to solve global optimization problems. It provides effective parallel realization. In this work we derive ADE with restart ADE-R. By increasing population size after each restart, new strategy enhances its chances to locate the global minimum. The ADE-R algorithm has convergence rate comparable or better than ADE with fixed population sizes. Performance of the ADE-R algorithm is demonstrated on a set of benchmark functions.