An estimation of distribution algorithm guided by mean shift

The estimation of distribution algorithm is widely used to solve global optimization problems in recent years. The basic idea is using machine learning methods to extract relevant features of the search space among the selected individuals and to construct a probabilistic model for sampling new solutions. As we know, EDAs mainly focus on the global distribution information of population and are lack of solution location information. In this paper, we extend our previous work to propose a new EDA guided by the mean shift method, which is originally proposed as a density estimation method and is used as a local search method in this paper. In the new approach, at first a set of candidate solutions are generated by EDA. Then the mean shift method is used to refine some good parent solutions. Finally the sampled candidate solutions and the refined solutions are combined to form the offspring solutions. By this way, the global distribution information and the solution location information are used in offspring reproduction. We apply the new approach to a set of test instances and the experiment results indicate that the new algorithm can obtain good performance in most functions with a faster convergence rate.