Enhanced Differential Evolution with Self-organizing Map for Numerical Optimization

In Differential evolution (DE), the valuable information from the data generated during the evolutionary process has not yet fully exploited to guide the search. As a clustering algorithm based on neural network structure, Self-organizing map (SOM) method can effectively preserve the topological structure of the data in the high dimensional input space. By taking the advantage of SOM, this paper presents a SOM-based DE variant (DE-SOM) to utilize the neighborhood information extracted by the SOM method. In DESOM, the neighborhood relationships among the individuals are firstly extracted by the SOM method. Then, with the obtained neighborhood relationships, a self-adaptive neighborhood mechanism (SNM) is introduced to dynamically adjust the neighborhood size for selecting parents involved in the mutation process. The performance of DE-SOM has been evaluated on the benchmark functions from CEC2013, and the results show its effectiveness when compared with the original DE algorithms.