基于多子群的社会群体优化算法

Social group optimization (SGO) is a novel optimization algorithm which is based on social group learning. This paper proposed a multi-group social group learning algorithm (MPSGO) to solve the problem that the social group optimization algorithm was easy convergent to local optima. This algorithm adopted the multi-subgroup learning method, and the improvement of the individual learning method in the two stages of the algorithm. The diversity of the population was improved with maintaining the convergence of the population. At the same time, it introduced quantum-behaved learning method into MPSGO for parts of individuals to enhance the useful information of individual learning. In addition, the population was randomly regrouped to generate new subgroups after a certain generation. The diversity of subgroups wais maintained and the individuals in every subgroup were fully evolved. Based on the designing of algorithm, this paper analyzed the algorithm of convergence and diversity. Compared with the other 4 algorithms, it is proved that the performance of the improved algorithm is better.