A genetic algorithm with SOM neural network clustering for multimodal function optimization

Identifying the number of niches in multimodal optimization is vital to enhancement of efficiency of algorithms. This paper presents a genetic algorithm (GA)-based clustering method for multiple optimal determinations. The approach uses self-organizing map (SOM) neural networks to detect clusters in GA population. After clustering all population and recognizing the number of niches, the phenotypic space is partitioned. Within each partition, a simple GA is independently running to evolve to the actual optima. Before the SOM starts, we allow GA to run several generations until the borders of clusters are identified. Our proposed algorithm is easy to implement, and does not require any prior knowledge about the fitness function. The algorithm was tested for seven multimodal functions and four constrained engineering optimization functions, and the results have been compared with the other related algorithms based on three performance criteria. We found that the present algorithm has acceptable diversification and function evaluation number.