Adapting the migration topology of macro-micro evolutionary algorithm by clustering the individuals using self-organizing map

In this paper, we propose a self-adaptive migration rule for macro-micro evolutionary algorithm which was proposed to find several local optima for multi-model optimization problems. The algorithm consists of two evolutionary algorithms which control global species and local individuals respectively. To keep the diversity explicitly, we incorporate a clustering method to divide individuals to several species. Clustering method based on self-organizing map (SOM) can divide individuals to several species and determine the neighboring topology information which defines the migration topology between species. To examine the computational effectiveness of proposed algorithm, we apply the algorithm to standard benchmark problems for numerical optimization