A smoothing evolutionary algorithm based on square search and filled function for global optimization

Many effective algorithms have been proposed for the global optimization problems arisen in various practical fields. However, some of these problems exist many local optima, which may lead to premature for solution algorithms. In order to avoid entrapping in the local optima, a smoothing function and square search method were used in the designed evolutionary algorithm. Using smoothing function can flatten the hilltops of the original function and eliminate all local optimal solutions which are no better than the best one found so far. Based on the smoothing function, square search scheme is presented, which can fall in a lower valley easier. Then, a filled function and local search were used to update the better solution found so far. Simulation results on 9 high dimensional standard benchmark problems indicate the performance of the proposed evolutionary algorithm is effective and sound.